Journal articles: 'Gaussian process mixture model'

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Tayal, Aditya, Pascal Poupart, and Yuying Li. "Hierarchical Double Dirichlet Process Mixture of Gaussian Processes." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1126–33. http://dx.doi.org/10.1609/aaai.v26i1.8309.

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We consider an infinite mixture model of Gaussian processes that share mixture components between non-local clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts. In this paper, we extend this approach to allow for experts to be shared non-locally across the input domain. This is accomplished with a hierarchical double Dirichlet process prior, which builds upon a standard hierarchical Dirichlet process by incorporating local parameters that are unique to each cluster while sharing mixture components between them. We evaluate the model on simulated and real data, showing that sharing Gaussian process components non-locally can yield effective and useful models for richly clustered non-stationary, non-linear data.

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Savitsky, Terrance, and Marina Vannucci. "Spiked Dirichlet Process Priors for Gaussian Process Models." Journal of Probability and Statistics 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/201489.

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We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates. Our approach results in a nonparametric treatment of the distribution of the covariance parameters of the GP covariance matrix that in turn induces a clustering of the covariates. We evaluate two prior constructions: the first one employs a mixture of a point-mass and a continuous distribution as the centering distribution for the DP prior, therefore, clustering all covariates. The second one employs a mixture of a spike and a DP prior with a continuous distribution as the centering distribution, which induces clustering of the selected covariates only. DP models borrow information across covariates through model-based clustering. Our simulation results, in particular, show a reduction in posterior sampling variability and, in turn, enhanced prediction performances. In our model formulations, we accomplish posterior inference by employing novel combinations and extensions of existing algorithms for inference with DP prior models and compare performances under the two prior constructions.

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Ay, Fahrettin, Gökhan İnce, Mustafa E. Kamaşak, and K. Yavuz Ekşi. "Classification of pulsars with Dirichlet process Gaussian mixture model." Monthly Notices of the Royal Astronomical Society 493, no. 1 (January 17, 2020): 713–22. http://dx.doi.org/10.1093/mnras/staa154.

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ABSTRACT Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analysing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature, and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provide hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.

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Yu, Jie, and S. Joe Qin. "Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring." Industrial & Engineering Chemistry Research 48, no. 18 (September 16, 2009): 8585–94. http://dx.doi.org/10.1021/ie900479g.

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Chen, Shutian, Qingchao Jiang, and Xuefeng Yan. "Multimodal process monitoring based on transition-constrained Gaussian mixture model." Chinese Journal of Chemical Engineering 28, no. 12 (December 2020): 3070–78. http://dx.doi.org/10.1016/j.cjche.2020.08.021.

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ZHANG, FENG, and ZHUJUN WENG. "MIXTURE PRINCIPAL COMPONENT ANALYSIS MODEL FOR MULTIVARIATE PROCESSES MONITORING." Journal of Advanced Manufacturing Systems 04, no. 02 (December 2005): 151–66. http://dx.doi.org/10.1142/s0219686705000631.

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A mixture probabilistic principal component analysis model is proposed as a process monitoring tool in this paper. High-dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters are automatically determined from the maximum likelihood estimation procedures. It was illustrated that the mixture PCA models conform to the multivariate data well in the experiments involving Gaussian mixtures. The multivariate statistical process monitoring mechanism is then developed first with the learning of a finite mixture model with variant principal component within each cluster, followed by the construction of the statistical process confidence intervals for the identified regions or nodes from T2 charts. For the abnormal input measurement, they would fall out of the acceptance region set by the confidence control limits.

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Guo, Wei, Tianhong Pan, Zhengming Li, and Shan Chen. "Batch process modeling by using temporal feature and Gaussian mixture model." Transactions of the Institute of Measurement and Control 42, no. 6 (December 1, 2019): 1204–14. http://dx.doi.org/10.1177/0142331219887827.

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Multi-model/multi-phase modeling algorithm has been widely used to monitor the product quality in complicated batch processes. Most multi-model/ multi-phase modeling methods hinge on the structure of a linearly separable space or a combination of different sub-spaces. However, it is impossible to accurately separate the overlapping region samples into different operating sub-spaces using unsupervised learning techniques. A Gaussian mixture model (GMM) using temporal features is proposed in the work. First, the number of sub-model is estimated by using the maximum interval process trend analysis algorithm. Then, the GMM parameters constrained with the temporal value are identified by using the expectation maximization (EM) algorithm, which minimizes confusion in overlapping regions of different Gaussian processes. A numerical example and a penicillin fermentation process demonstrate the effectiveness of the proposed algorithm.

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Zheng, Junhua, Qiaojun Wen, and Zhihuan Song. "Recursive Gaussian Mixture Models for Adaptive Process Monitoring." Industrial & Engineering Chemistry Research 58, no. 16 (April 2019): 6551–61. http://dx.doi.org/10.1021/acs.iecr.8b06101.

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Yuhan, Zhao. "Gaussian process mixture model for prediction based on maximum posterior distribution." Journal of Physics: Conference Series 2014, no. 1 (September 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2014/1/012007.

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Li, Ling-Ling, Jin Sun, Ching-Hsin Wang, Ya-Tong Zhou, and Kuo-Ping Lin. "Enhanced Gaussian process mixture model for short-term electric load forecasting." Information Sciences 477 (March 2019): 386–98. http://dx.doi.org/10.1016/j.ins.2018.10.063.

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Tian, Jinkai, Peifeng Yan, and Da Huang. "Kernel Analysis Based on Dirichlet Processes Mixture Models." Entropy 21, no. 9 (September 2, 2019): 857. http://dx.doi.org/10.3390/e21090857.

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Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels. However, the number of components in a GMM is fixed. Thus, this model suffers from overfitting or underfitting. In this paper, we try to combine the spectral domain of kernels with nonparametric Bayesian models. Dirichlet processes mixture models are used to resolve this problem by changing the number of components according to the data size. Multiple experiments have been conducted on this model and it shows competitive performance.

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Li, Zhe, Chen Ma, and Tian-Fan Zhang. "Depth Data Reconstruction Based on Gaussian Mixture Model." Cybernetics and Information Technologies 16, no. 6 (December 1, 2016): 207–19. http://dx.doi.org/10.1515/cait-2016-0089.

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Abstract Depth data is an effective tool to locate the intelligent agent in space because it accurately records the 3D geometry information on the surface of the scanned object, and is not affected by factors like shadow and light. However, if there are many planes in the work scene, it is difficult to identify objects and process the resulting huge amount of data. In view of this problem and targeted at object calibration, this paper puts forward a depth data calibration method based on Gauss mixture model. The method converts the depth data to point cloud, filters the noise and collects samples, which effectively reduces the computational load in the following steps. Besides, the authors cluster the point cloud vector with the Gaussian mixture model, and obtain the target and background planes by using the random sampling consensus algorithm to fit the planes. The combination of target Region Of Intelligent agent (ROI) and point cloud significantly reduces the computational load and improves the computing speed. The effect and accuracy of the algorithm is verified by the test of the actual object.

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Mao, Runjun, Chengdong Cao, James Jing Yue Qian, Jiufan Wang, and Yunpeng Liu. "Mixture of Gaussian Processes Based on Bayesian Optimization." Journal of Sensors 2022 (September 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/7646554.

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This paper gives a detailed introduction of implementing mixture of Gaussian process (MGP) model and develops its application for Bayesian optimization (BayesOpt). The paper also develops techniques for MGP in finding its mixture components and introduced an alternative gating network based on the Dirichlet distributions. BayesOpt using the resultant MGP model significantly outperforms the one based on Gaussian process regression in terms of optimization efficiency in the test on tuning the hyperparameters in common machine learning algorithms. This indicates the success of the methods, implying a promising future of wider application for MGP model and the BayesOpt based on it.

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Xu, Jiawei, and Qian Luo. "Human action recognition based on mixed gaussian hidden markov model." MATEC Web of Conferences 336 (2021): 06004. http://dx.doi.org/10.1051/matecconf/202133606004.

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Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.

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Wei, Hongchuan, Wenjie Lu, Pingping Zhu, Silvia Ferrari, Miao Liu, Robert H. Klein, Shayegan Omidshafiei, and Jonathan P. How. "Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models." Automatica 74 (December 2016): 360–68. http://dx.doi.org/10.1016/j.automatica.2016.07.018.

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Chen, Guang Hua, and Gui Zhi Sheng. "Detection of Moving Objects Based on Mixture Gaussian Model." Advanced Materials Research 1039 (October 2014): 274–79. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.274.

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The paper proposes an improved adaptive Gaussian mixture model (GMM) approach with online EM algorithms for updating, which solves the video segmentation problems carried by busy environment and illumination change. Different learning rates are set for foreground district and background district respectively, which improves the convergence speed of background model. A shadow removal scheme is also introduced for extracting complete moving objects. It is based on brightness distortion and chromaticity distortion in RGB color space. Morphological filtering and connected components analysis algorithm are also introduced to process the result of background subtraction. The experiment results show that the improved GMM has good accuracy and high adaptability in video segmentation. It can extract a complete and clear moving object when it is incorporated with shadow removal.

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Alfina, Alfina, and Dzulgunar Muhammad Nasir. "Model Identifikasi Pemalsuan Ijazah menggunakan Gabor Wavelet dan Gaussian Mixture Models Super Vektor (GMM-SV)." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 4, no. 2 (August 29, 2020): 87. http://dx.doi.org/10.35870/jtik.v4i2.142.

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Various cases occur related to certificate falsification and some people and educational institutions have to deal with the law, this problem is not impossible to abuse along with advances and technological innovation with various tools that can be used by anyone. Identifying the diploma document must be of particular concern to tertiary institutions to minimize the associated fake diplomas and the diploma legalization process. In legalizing the diploma for STMIK Indonesia Banda Aceh students, checking the authenticity of the certificate is only by bringing the original certificate and photocopy of the certificate or by contacting the academic party who issued the certificate, this process is sometimes missed by officers when the queue is crowded. The specific objectives of the research include implementing a model and feature method of Gabor Wavelet and Gaussian Mixture Models Super Vector (GMM-SV) for document identification to speed up diploma identification. The flow of this research starts from the input in the form of a basic image as an image that a reference for the authenticity of the diploma. Then the test image input is an image that will be tested for authenticity. The results showed that using the Gabor Wavelet feature and the Gaussian Mixture Models Super Vector (GMM-SV) could identify fake diplomas with an accuracy rate of 92.8%.Keywords:Model, Identification, Certificate Falsification, Gabor Wavelet, Gaussian Mixture Models Super Vector (GMM-SV).

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Chapaneri, Santosh, and Deepak Jayaswal. "Structured Gaussian Process Regression of Music Mood." Fundamenta Informaticae 176, no. 2 (December 18, 2020): 183–203. http://dx.doi.org/10.3233/fi-2020-1970.

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Modeling the music mood has wide applications in music categorization, retrieval, and recommendation systems; however, it is challenging to computationally model the affective content of music due to its subjective nature. In this work, a structured regression framework is proposed to model the valence and arousal mood dimensions of music using a single regression model at a linear computational cost. To tackle the subjectivity phenomena, a confidence-interval based estimated consensus is computed by modeling the behavior of various annotators (e.g. biased, adversarial) and is shown to perform better than using the average annotation values. For a compact feature representation of music clips, variational Bayesian inference is used to learn the Gaussian mixture model representation of acoustic features and chord-related features are used to improve the valence estimation by probing the chord progressions between chroma frames. The dimensionality of features is further reduced using an adaptive version of kernel PCA. Using an efficient implementation of twin Gaussian process for structured regression, the proposed work achieves a significant improvement in R2 for arousal and valence dimensions relative to state-of-the-art techniques on two benchmark datasets for music mood estimation.

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Yu, Jie, Kuilin Chen, Junichi Mori, and Mudassir M. Rashid. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction." Energy 61 (November 2013): 673–86. http://dx.doi.org/10.1016/j.energy.2013.09.013.

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COZZINI, ALBERTO, AJAY JASRA, and GIOVANNI MONTANA. "MODEL-BASED CLUSTERING WITH GENE RANKING USING PENALIZED MIXTURES OF HEAVY-TAILED DISTRIBUTIONS." Journal of Bioinformatics and Computational Biology 11, no. 03 (June 2013): 1341007. http://dx.doi.org/10.1142/s0219720013410072.

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Cluster analysis of biological samples using gene expression measurements is a common task which aids the discovery of heterogeneous biological sub-populations having distinct mRNA profiles. Several model-based clustering algorithms have been proposed in which the distribution of gene expression values within each sub-group is assumed to be Gaussian. In the presence of noise and extreme observations, a mixture of Gaussian densities may over-fit and overestimate the true number of clusters. Moreover, commonly used model-based clustering algorithms do not generally provide a mechanism to quantify the relative contribution of each gene to the final partitioning of the data. We propose a penalized mixture of Student's t distributions for model-based clustering and gene ranking. Together with a resampling procedure, the proposed approach provides a means for ranking genes according to their contributions to the clustering process. Experimental results show that the algorithm performs well comparably to traditional Gaussian mixtures in the presence of outliers and longer tailed distributions. The algorithm also identifies the true informative genes with high sensitivity, and achieves improved model selection. An illustrative application to breast cancer data is also presented which confirms established tumor sub-classes.

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Rantini, Dwi, Nur Iriawan, and Irhamah Irhamah. "On the Reversible Jump Markov Chain Monte Carlo (RJMCMC) Algorithm for Extreme Value Mixture Distribution as a Location-Scale Transformation of the Weibull Distribution." Applied Sciences 11, no. 16 (August 10, 2021): 7343. http://dx.doi.org/10.3390/app11167343.

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Data with a multimodal pattern can be analyzed using a mixture model. In a mixture model, the most important step is the determination of the number of mixture components, because finding the correct number of mixture components will reduce the error of the resulting model. In a Bayesian analysis, one method that can be used to determine the number of mixture components is the reversible jump Markov chain Monte Carlo (RJMCMC). The RJMCMC is used for distributions that have location and scale parameters or location-scale distribution, such as the Gaussian distribution family. In this research, we added an important step before beginning to use the RJMCMC method, namely the modification of the analyzed distribution into location-scale distribution. We called this the non-Gaussian RJMCMC (NG-RJMCMC) algorithm. The following steps are the same as for the RJMCMC. In this study, we applied it to the Weibull distribution. This will help many researchers in the field of survival analysis since most of the survival time distribution is Weibull. We transformed the Weibull distribution into a location-scale distribution, which is the extreme value (EV) type 1 (Gumbel-type for minima) distribution. Thus, for the mixture analysis, we call this EV-I mixture distribution. Based on the simulation results, we can conclude that the accuracy level is at minimum 95%. We also applied the EV-I mixture distribution and compared it with the Gaussian mixture distribution for enzyme, acidity, and galaxy datasets. Based on the Kullback–Leibler divergence (KLD) and visual observation, the EV-I mixture distribution has higher coverage than the Gaussian mixture distribution. We also applied it to our dengue hemorrhagic fever (DHF) data from eastern Surabaya, East Java, Indonesia. The estimation results show that the number of mixture components in the data is four; we also obtained the estimation results of the other parameters and labels for each observation. Based on the Kullback–Leibler divergence (KLD) and visual observation, for our data, the EV-I mixture distribution offers better coverage than the Gaussian mixture distribution.

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Ozaki, Tohru, and Mitsunori Iino. "An innovation approach to non-Gaussian time series analysis." Journal of Applied Probability 38, A (2001): 78–92. http://dx.doi.org/10.1239/jap/1085496593.

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The paper shows that the use of both types of random noise, white noise and Poisson noise, can be justified when using an innovations approach. The historical background for this is sketched, and then several methods of whitening dependent time series are outlined, including a mixture of Gaussian white noise and a compound Poisson process: this appears as a natural extension of the Gaussian white noise model for the prediction errors of a non-Gaussian time series. A statistical method for the identification of non-linear time series models with noise made up of a mixture of Gaussian white noise and a compound Poisson noise is presented. The method is applied to financial time series data (dollar-yen exchange rate data), and illustrated via six models.

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Ozaki, Tohru, and Mitsunori Iino. "An innovation approach to non-Gaussian time series analysis." Journal of Applied Probability 38, A (2001): 78–92. http://dx.doi.org/10.1017/s0021900200112690.

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The paper shows that the use of both types of random noise, white noise and Poisson noise, can be justified when using an innovations approach. The historical background for this is sketched, and then several methods of whitening dependent time series are outlined, including a mixture of Gaussian white noise and a compound Poisson process: this appears as a natural extension of the Gaussian white noise model for the prediction errors of a non-Gaussian time series. A statistical method for the identification of non-linear time series models with noise made up of a mixture of Gaussian white noise and a compound Poisson noise is presented. The method is applied to financial time series data (dollar-yen exchange rate data), and illustrated via six models.

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Lu, Ziwei, and Hongfei Yu. "Single Image Super-Resolution Algorithm based on Fixed-Point Multi-Model Gaussian Process Regression." Journal of Physics: Conference Series 2289, no. 1 (June 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2289/1/012024.

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Abstract To solve the problem of large amount of computation in matrix inversion of Gaussian process regression model, a super-resolution algorithm based on local Gaussian process regression of fixed point multi-model is proposed. Firstly, the training samples are classified by Gaussian mixture model, and image patches are randomly selected as fixed points in each type of training samples, and its K nearest neighbor patch are searched. Secondly, local Gaussian process regression model by using its low-mid-frequency components and the corresponding high frequency components. Again, low resolution test image patch is classified, only the K nearest neighbor test image patch is searched during reconstruction. And then, find the nearest fixed point in each kind of image patch, and use the local Gaussian process regression model based on the fixed point to predict its corresponding high-frequency component. Finally, corresponding high frequency information is predicted by using this trained model, which improves the reconstruction efficiency. Experimental results have demonstrated that the proposed algorithm is superior in both quantitative and qualitative aspects against other algorithms.

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Li, Zhenglin, Lyudmila S. Mihaylova, Olga Isupova, and Lucile Rossi. "Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model." IEEE Transactions on Industrial Informatics 14, no. 3 (March 2018): 1146–54. http://dx.doi.org/10.1109/tii.2017.2768530.

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McDowell, Ian C., Dinesh Manandhar, Christopher M. Vockley, Amy K. Schmid, Timothy E. Reddy, and Barbara E. Engelhardt. "Clustering gene expression time series data using an infinite Gaussian process mixture model." PLOS Computational Biology 14, no. 1 (January 16, 2018): e1005896. http://dx.doi.org/10.1371/journal.pcbi.1005896.

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Yi, Junkai, Guanglin Gong, Zeyu Liu, and Yacong Zhang. "Classification of Markov Encrypted Traffic on Gaussian Mixture Model Constrained Clustering." Wireless Communications and Mobile Computing 2021 (October 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/4935108.

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In order to solve the problem that traditional analysis approaches of encrypted traffic in encryption transmission of network application only consider the traffic classification in the complete communication process with ignoring traffic classification in the simplified communication process, and there are a lot of duplication problems in application fingerprints during state transition, a new classification approach of encrypted traffic is proposed. The article applies the Gaussian mixture model (GMM) to analyze the length of the message, and the model is established to solve the problem of application fingerprint duplication. The fingerprints with similar lengths of the same application are divided into as few clusters as possible by constrained clustering approach, which speeds up convergence speed and improves the clustering effect. The experimental results show that compared with the other encryption traffic classification approaches, the proposed approach has 11.7%, 19.8%, 6.86%, and 5.36% improvement in TPR, FPR, Precision, and Recall, respectively, and the classification effect of encrypted traffic is significantly improved.

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Görür, Dilan, and Carl Edward Rasmussen. "Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution." Journal of Computer Science and Technology 25, no. 4 (July 2010): 653–64. http://dx.doi.org/10.1007/s11390-010-9355-8.

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Chen, Zhongsheng, Yongmin Yang, Zheng Hu, and Qinghu Zeng. "Fault prognosis of complex mechanical systems based on multi-sensor mixtured hidden semi-Markov models." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 8 (November 21, 2012): 1853–63. http://dx.doi.org/10.1177/0954406212467260.

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Accurate fault prognosis is of vital importance for condition-based maintenance. As to complex mechanical systems, multiple sensors are often used to collect condition signals and the observation process may rather be non-Gaussian and non-stationary. Traditional hidden semi-Markov models cannot provide adequate representation for multivariate non-Gaussian and non-stationary time series. The innovation of this article is to extend classical hidden semi-Markov models by modeling the observation as a linear mixture of non-Gaussian multi-sensor signals. The proposed model is called as a multi-sensor mixtured hidden semi-Markov model. Under this new framework, modified parameter re-estimation algorithms are derived in detail based on the complete-data expectation maximization algorithm. In the end the proposed prognostic methodology is validated on a practical bearing application. The experimental results show that the proposed method is indeed promising to obtain better prognostic performance than classical hidden semi-Markov models.

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Alimisis, Vassilis, Georgios Gennis, Konstantinos Touloupas, Christos Dimas, Nikolaos Uzunoglu, and Paul P. Sotiriadis. "Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction." Bioengineering 9, no. 4 (April 5, 2022): 160. http://dx.doi.org/10.3390/bioengineering9040160.

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This paper presents a new analog front-end classification system that serves as a wake-up engine for digital back-ends, targeting embedded devices for epileptic seizure prediction. Predicting epileptic seizures is of major importance for the patient’s quality of life as they can lead to paralyzation or even prove fatal. Existing solutions rely on power hungry embedded digital inference engines that typically consume several µW or even mW. To increase the embedded device’s autonomy, a new approach is presented combining an analog feature extractor with an analog Gaussian mixture model-based binary classifier. The proposed classification system provides an initial, power-efficient prediction with high sensitivity to switch on the digital engine for the accurate evaluation. The classifier’s circuit is chip-area efficient, operating with minimal power consumption (180 nW) at low supply voltage (0.6 V), allowing long-term continuous operation. Based on a real-world dataset, the proposed system achieves 100% sensitivity to guarantee that all seizures are predicted and good specificity (69%), resulting in significant power reduction of the digital engine and therefore the total system. The proposed classifier was designed and simulated in a TSMC 90 nm CMOS process, using the Cadence IC suite.

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Chakraborty, Biswanath, Siddhartha Bhattacharyya, and Susanta Chakraborty. "Generative Model Based Video Shot Boundary Detection for Automated Surveillance." International Journal of Ambient Computing and Intelligence 9, no. 4 (October 2018): 69–95. http://dx.doi.org/10.4018/ijaci.2018100105.

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Video shot boundary detection (SBD) or video cut detection is one of the fundamental processes of video-processing with respect to semantic understanding, contextual information accumulation, labeling, content-based information retrieval and many more applications, such as video surveillance and monitoring. In this work, the authors have proposed a generative-model based framework for detecting shot boundaries in between the frames of a video segment. To generate a model of shot-boundaries, the authors have applied the concepts of Support Vector Machine to estimate the distance between any two images, and then, have generated a Gaussian Mixture Model from the estimated distances. Next, a Bayesian Estimation process checks the presence of boundaries in between the images by exploiting the Gaussian Mixture-based boundary model. Further, the authors have used the principles of Compressive Sensing to reduce the overhead of boundary detection process without losing of important information.

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Gani, Prati Hutari, and Gusti Ayu Putri Saptawati. "Pengembangan Model Fast Incremental Gaussian Mixture Network (IGMN) pada Interpolasi Spasial." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 1 (January 25, 2022): 507. http://dx.doi.org/10.30865/mib.v6i1.3490.

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Gathering geospatial information in an organization is one of the most critical processes to support decision-making and business sustainability. However, many obstacles can hinder this process, like uncertain natural conditions and a large geographical area. This problem causes the organization only to obtain a few sample points of observation, resulting in incomplete information. The data incompleteness problem can be solved by applying spatial interpolation to estimate or determine the value of unavailable data. Spatial interpolation generally uses geostatistical methods. These geostatistical methods require a variogram as a model built based on the knowledge and input of geostatistic experts. The existence of this variogram becomes a necessity to implement these methods. However, it becomes less suitable to be applied to organizations that do not have geostatistics experts. This research will develop a Fast IGMN model in solving spatial interpolation. In this study, results of the modified Fast IGMN model in spatial interpolation increase the interpolation accuracy. Fast IGMN without modification produces MSE = 1.234429691, while using Modified Fast IGMN produces MSE = 0.687391. The MSE value of the Fast IGMN-Modification model is smaller, which means that the smaller the MSE value, the higher the accuracy of the interpolation results. This modified Fast IGMN model can solve problems in gathering information for an organization that does not have geostatistics experts in the spatial data modeling process. However, it needs to be developed again with more varied input data.

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Khan, Junaid Bahadar, Tariqullah Jan, Ruhul Amin Khalil, Nasir Saeed, and Muhannad Almutiry. "An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture." Applied Sciences 11, no. 13 (June 27, 2021): 5968. http://dx.doi.org/10.3390/app11135968.

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This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low- and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, the two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

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Cho, W. H., S. K. Kim, and S. Y. Park. "Human Action Recognition Using Hybrid Method of Hidden Markov Model and Dirichlet Process Gaussian Mixture Model." Advanced Science Letters 23, no. 3 (March 1, 2017): 1652–55. http://dx.doi.org/10.1166/asl.2017.8599.

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Mulimani, neshwari, and Aziz Makandar. "Sports Video Annotation and Multi- Target Tracking using Extended Gaussian Mixture model." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 1–6. http://dx.doi.org/10.35940/ijrte.a5589.0510121.

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Video offers solutions to many of the traditional problems with coach, trainer, commenter, umpires and other security issues of modern team games. This paper presents a novel framework to perform player identification and tracking technique for the sports (Kabaddi) with extending the implementation towards the event handling process which expands the game analysis of the third umpire assessment. In the proposed methodology, video preprocessing has done with Kalman Filtering (KF) technique. Extended Gaussian Mixture Model (EGMM) implemented to detect the object occlusions and player labeling. Morphological operations have given the more genuine results on player detection on the spatial domain by applying the silhouette spot model. Team localization and player tracking has done with Robust Color Table (RCT) model generation to classify each team members. Hough Grid Transformation (HGT) and Region of Interest (RoI) method has applied for background annotation process. Through which each court line tracing and labeling in the half of the court with respect to their state-of-art for foremost event handling process is performed. Extensive experiments have been conducted on real time video samples to meet out the all the challenging aspects. Proposed algorithm tested on both Self Developed Video (SDV) data and Real Time Video (RTV) with dynamic background for the greater tracking accuracy and performance measures in the different state of video samples.

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PAL, AMITA, SMARAJIT BOSE, GOPAL K. BASAK, and AMITAVA MUKHOPADHYAY. "SPEAKER IDENTIFICATION BY AGGREGATING GAUSSIAN MIXTURE MODELS (GMMs) BASED ON UNCORRELATED MFCC-DERIVED FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 04 (June 2014): 1456006. http://dx.doi.org/10.1142/s0218001414560060.

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For solving speaker identification problems, the approach proposed by Reynolds [IEEE Signal Process. Lett.2 (1995) 46–48], using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, is one of the most effective available in the literature. The use of GMMs for modeling speaker identity is motivated by the interpretation that the Gaussian components represent some general speaker-dependent spectral shapes, and also by the capability of Gaussian mixtures to model arbitrary densities. In this work, we have initially illustrated, with the help of a new bilingual speech corpus, how the well-known principal component transformation, in conjunction with the principle of classifier combination can be used to enhance the performance of the MFCC-GMM speaker recognition systems significantly. Subsequently, we have emphatically and rigorously established the same using the benchmark speech corpus NTIMIT. A significant outcome of this work is that the proposed approach has the potential to enhance the performance of any speaker recognition system based on correlated features.

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Niu, Guochen, Licheng Wang, and Zheng Tan. "Mismatch Removal Based on Gaussian Mixture Model for Aircraft Surface Texture Mapping." Information Technology And Control 49, no. 1 (March 25, 2020): 80–87. http://dx.doi.org/10.5755/j01.itc.49.1.22855.

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Aiming at the fact of lower efficiency and higher time cost for feature matching in aircraft surface texture mapping process, a novel mismatch removal method based on Gaussian mixture model is proposed to increase correct corresponding feature matching point pairs. The detection and initial point sets for corresponding pairs are carried out, and a vector field is interpolated between the two matching of ORB feature points. The Gaussian mixture model(GMM) is introduced and a prior is taken to force the smoothness of the field, which is based on the Tikhonov regularization in vector-valued reproducing kernel Hilbert space(RKHS). In order to obtain the optimal estimation, the MAP solution of a Bayesian model with latent variables, which could be performed by Expectation Maximization (EM) algorithm, is utilized to determine the correct correspondence. The experimental results show that the algorithm could remove mismatches effectively and the classification for feature points is excellent. The calculation time is greatly reduced, which enhanced real-time performance of aircraft surface texture mapping process.

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Chen, Tao, Julian Morris, and Elaine Martin. "Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring." Journal of the Royal Statistical Society: Series C (Applied Statistics) 55, no. 5 (November 2006): 699–715. http://dx.doi.org/10.1111/j.1467-9876.2006.00560.x.

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Rajkowski, Łukasz. "Analysis of the Maximal a Posteriori Partition in the Gaussian Dirichlet Process Mixture Model." Bayesian Analysis 14, no. 2 (June 2019): 477–94. http://dx.doi.org/10.1214/18-ba1114.

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Choi, Sang Wook, Jin Hyun Park, and In-Beum Lee. "Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis." Computers & Chemical Engineering 28, no. 8 (July 2004): 1377–87. http://dx.doi.org/10.1016/j.compchemeng.2003.09.031.

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Nafiudin, Iklillurofi Akbar, Rahmat Tofik Hidayat, Ajeng Mustika Putri, and Ahfas Reza Maulana. "DETEKSI JUMLAH KENDARAAN DENGAN ALGORITMA GAUSSIAN MIXTURE MODEL DI AREA JALAN RAYA." METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi 7, no. 1 (March 10, 2021): 37–44. http://dx.doi.org/10.46880/mtk.v7i1.258.

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Road safety monitoring systems are developing at this time. The transportation sector is the object of research that continues to be developed and is always an interesting topic. Not only for security purposes and for statistical purposes for the road widening process that supports road user infrastructure, but the detection system is also useful for sales marketing statistics. In this research, propose a vehicle detection system that is useful for widening roads in a certain area or area so that it can reduce traffic congestion and accident rates. The proposed Gaussian Mixture Model method has several weaknesses, such as errors in background substitution with vehicles and failing to distribute the background with vehicle shadows. However, using morphological operations can overcome these problems. The results show a fairly good level of accuracy from the proposed method. It is only less effective when using video objects with poor lighting or at night because in the blob analysis process the detected vehicle objects do not match the actual object. But if the traffic flow is smooth and unidirectional, the proposed method is still acceptable.

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Alameri, Mohammed, Khairunnisa Hasikin, Nahrizul Adib Kadri, Nashrul Fazli Mohd Nasir, Prabu Mohandas, Jerline Sheeba Anni, and Muhammad Mokhzaini Azizan. "Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking." Computational and Mathematical Methods in Medicine 2021 (August 29, 2021): 1–14. http://dx.doi.org/10.1155/2021/6953593.

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Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.

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Wang, Wei, Meng Hang Zhang, Shuang Quan Guo, Hui Li, Wei Lv, Jia Rong Yang, and Zong Chang Liu. "Global Performance Estimation Based on Gaussian Mixture Model for Wind Turbines." Applied Mechanics and Materials 670-671 (October 2014): 1033–36. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1033.

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In order to ensure that the wind turbines are reliable in stable condition and economical in maintenance cost, the most effective way is to estimate and monitor the performance and operation of the wind turbine. Traditional fault diagnosis methods using multivariate statistical process usually assume the unit only has a single operating condition, so it’s not suitable for multi-regimes. Aiming at this problem, this paper proposed a global performance estimation method of multi-regimes condition based on Gaussian mixture model (GMM). First establish GMM to train the baseline model, cluster the sample data using the similar GMM method, and then calculate the distance between the baseline model and the GMM of sample data by two different methods. The result shows that this method can identify the characteristics of the turbine productivity well, and can detect the abnormality of power curve that is related to incipient fault.

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Xie, Xiang, and Hongbo Shi. "Dynamic Multimode Process Modeling and Monitoring Using Adaptive Gaussian Mixture Models." Industrial & Engineering Chemistry Research 51, no. 15 (April 6, 2012): 5497–505. http://dx.doi.org/10.1021/ie202720y.

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Yu, Jie, and S. Joe Qin. "Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models." AIChE Journal 54, no. 7 (2008): 1811–29. http://dx.doi.org/10.1002/aic.11515.

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Sun, J., Gang Yu, and Chang Ning Li. "Bearing Fault Diagnosis Using Gaussian Mixture Models (GMMs)." Applied Mechanics and Materials 10-12 (December 2007): 553–57. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.553.

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This paper presents a novel method for bearing fault diagnosis based on wavelet transform and Gaussian mixture models (GMMs). Vibration signals for normal bearings, bearings with inner race faults, outer race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the vibration signals and to generate feature vectors. GMMs were trained and used as a diagnostic classifier. Experimental results have shown that GMMs can reliably classify different fault conditions and have a better classification performance as compared to the multilayer perceptron neural networks.

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S, Charles, and Dr L. Arockiam. "Fuzzy Weighted Ordered Weighted Average-Gaussian Mixture Model for Feature Reduction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (November 30, 2005): 694–712. http://dx.doi.org/10.24297/ijct.v4i2c2.4192.

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Feature reduction finds the optimal feature subset using machine learning techniques and evaluation criteria. Some of the irrelevant features are existed in the real-world datasets that should be removed by using the multi criterion decision approach. The relevant features are determined by using the WOWA criteria in fuzzy set. There are two important criteria are considered such as preferential weights and importance weights of features. These weights are used to find the irrelevant features and they are removed from the mixture. In this context, WOWA operator has the capability of assigning the preferential weights and important weights to the features. It helps to obtain the irrelevant, by selecting the relevant features using the weights in the feature reduction process. The objective of this paper is to propose a FWOWA approach helps to discard the irrelevant features by avoiding the overfitting and improve the accuracy of the cluster. The irrelevant features are determined by applying WOWA. By applying WOWA, the irrelevant features are examined and it is removed from the Gaussian Mixture using (RPEM).Â Â Â

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Yang, Bin. "Shared Parts Latent Topic Model for Image Classification." Advanced Materials Research 271-273 (July 2011): 1257–62. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1257.

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This paper addresses the problem of accurately classifying image categories without any human interaction. A shared parts latent topic model is presented to share mixture components between categories. Different categories share the similar parts which make the model more accurate. As the number of components is unknown and is to be inferred from the train set, the Dirichlet process is introduced into the model to provide a nonparametric prior for the number of mixture components within each category. Gaussian mixture model is adopted to present the object color feature and the Wishart distribution is applied to estimate the parameters of object shape feature. A number of classification experiments are used to verify the success of our model.

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Yang, Wanhui, Hengyu Li, Jingyi Liu, Shaorong Xie, and Jun Luo. "A sea-sky-line detection method based on Gaussian mixture models and image texture features." International Journal of Advanced Robotic Systems 16, no. 6 (November 1, 2019): 172988141989211. http://dx.doi.org/10.1177/1729881419892116.

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This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.

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Zhu, Weijin, Yao Shen, Mingqian Liu, and Lizeth Patricia Aguirre Sanchez. "GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture Model." Computational Intelligence and Neuroscience 2022 (July 18, 2022): 1–13. http://dx.doi.org/10.1155/2022/7254462.

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Recent studies on unsupervised object detection based on spatial attention have achieved promising results. Models, such as AIR and SPAIR, output “what” and “where” latent variables that represent the attributes and locations of objects in a scene, respectively. Most of the previous studies concentrate on the “where” localization performance. However, we claim that acquiring “what” object attributes is also essential for representation learning. This study presents a framework, GMAIR, for unsupervised object detection. It incorporates spatial attention and a Gaussian mixture in a unified deep generative model. GMAIR can locate objects in a scene and simultaneously cluster them without supervision. Furthermore, we analyze the “what” latent variables and clustering process. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets. We show that GMAIR achieves competitive results on localization and clustering compared with state-of-the-art methods.

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Journal articles on the topic 'Gaussian process mixture model'

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