Journal articles: 'PCA ALGORITHM'

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Dinanti, Aldila, and Joko Purwadi. "Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis." Jambura Journal of Mathematics 5, no. 1 (February 1, 2023): 155–65. http://dx.doi.org/10.34312/jjom.v5i1.17098.

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This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reduction using PCA produces 3 main components of the 8 variables in the original data, namely PC1, PC2, and PC3. Then classification result using K-Nearest Neighbor shows that by choosing 3 closest neighbor parameters (K), for K = 3, K = 5, and K = 7. The result for K = 3 has an accuracy of 67,53%, for K = 5 had an accuracy is 72,72%, and for K=7 had an accuracy of 77,92%. Thus, it was concluded that the best accuracy performance for the classification of diabetes was achieved at K=7 with an accuracy of 77.92%.

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Subramaniam, Ashwin, and Byung-Joo Oh. "Mushroom Recognition Using PCA Algorithm." International Journal of Software Engineering and Its Applications 10, no. 1 (January 31, 2016): 43–50. http://dx.doi.org/10.14257/ijseia.2016.10.1.05.

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Subiyanto, Subiyanto, Dina Priliyana, Moh Eki Riyadani, Nur Iksan, and Hari Wibawanto. "Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (May 25, 2020): 210–16. http://dx.doi.org/10.14710/jtsiskom.2020.13590.

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Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.

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Zhang, Kang, Yongdong Huang, and Cheng Zhao. "Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (September 2018): 1850037. http://dx.doi.org/10.1142/s0219691318500376.

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In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for the high frequency subbands, the fusion rule employed is based on adaptive PCNN model. Finally, the fusion image is obtained by inverse NSST transform and inverse PCA transform. The experimental results illustrate that the proposed fusion algorithm outperforms other classical fusion algorithms (PCA, Curvelet, NSCT, NSST and NSCT-PCNN) in terms of visual quality and objective evaluation in whole, and achieve better fusion performance.

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Hadiprakoso, Raden Budiarto, and I. Komang Setia Buana. "Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition." IJICS (International Journal of Informatics and Computer Science) 5, no. 3 (November 30, 2021): 250. http://dx.doi.org/10.30865/ijics.v5i3.3333.

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Face recognition is a highly active research topic in pattern recognition and computer vision, with numerous practical applications. Face recognition can provide the most natural interaction experience similar to the way humans can recognize others. This paper presents a performance comparison of various machine learning approaches and feature extraction algorithms. The feature extraction algorithm used is Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), and a combination of PCA-LDA. The method used is to take a dataset sample and then evaluate and compare machine learning algorithms to analyze accuracy in recognizing faces. We also use feature extraction techniques on facial image capture to speed up data processing. The classification algorithms measured are k-nearest neighbor, naive Bayes, support vector machine, random forest, and gradient boosting. The results showed that the random forest classification algorithm was the most accurate face recognition method. On the other hand, the PCA-LDA combined feature extraction algorithm has lower false-negative and false-positive rates than PCA and LDA. In addition, the PCA feature extraction algorithm has the fastest performance in the process of recognizing faces

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Dolan, Matthew T., Sung Kim, Yu-Hsuan Shao, and Grace L. Lu-Yao. "Authentication of Algorithm to Detect Metastases in Men with Prostate Cancer Using ICD-9 Codes." Epidemiology Research International 2012 (August 22, 2012): 1–7. http://dx.doi.org/10.1155/2012/970406.

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Background. Metastasis is a crucial endpoint for patients with prostate cancer (PCa), but currently lacks a validated claims-based algorithm for detection. Objective. To develop an algorithm using ICD-9 codes to facilitate accurate reporting of PCa metastases. Methods. Medical records from 300 men hospitalized at Robert Wood Johnson University Hospital for PCa were reviewed. Using the presence of metastatic PCa on chart review as the gold standard, two algorithms to detect metastases were compared. Algorithm A used ICD-9 codes 198.5 (bone metastases), 197.0 (lung metastases), 197.7 (liver metastases), or 198.3 (brain and spinal cord metastases) to detect metastases, while algorithm B used only 198.5. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the two algorithms were determined. Kappa statistics were used to measure agreement rates between claim data and chart review. Results. Algorithm A demonstrated a sensitivity, specificity, PPV, and NPV of 95%, 100%, 100%, and 98.7%, respectively. Corresponding numbers for algorithm B were 90%, 100%, 100%, and 97.5%, respectively. The agreement rate is 96.8% for algorithm A and 93.5% for algorithm B. Conclusions. Using ICD-9 codes 198.5, 197.0, 197.7, or 198.3 in detecting the presence of PCa metastases offers a high sensitivity, specificity, PPV, and NPV value.

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Zhao, Wenjing, Yue Chi, Yatong Zhou, and Cheng Zhang. "Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1259703.

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SGK (sequential generalization of K-means) dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA) noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1) The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2) The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3) Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

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Li, Mingfei, Zhengpeng Chen, Jiangbo Dong, Kai Xiong, Chuangting Chen, Mumin Rao, Zhiping Peng, Xi Li, and Jingxuan Peng. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems." Energies 15, no. 7 (March 31, 2022): 2556. http://dx.doi.org/10.3390/en15072556.

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In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

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Vu, L. G., Abeer Alsadoon, P. W. C. Prasad, and A. M. S. Rahma. "Improving Accuracy in Face Recognition Proposal to Create a Hybrid Photo Indexing Algorithm, Consisting of Principal Component Analysis and a Triangular Algorithm (PCAaTA)." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 01 (January 2017): 1756001. http://dx.doi.org/10.1142/s0218001417560018.

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Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect the accuracy of this algorithm. The PCA works well with the support of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have shown results when using a combination of PCA and preprocessing steps. This paper proposes a hybrid solution in face recognition using PCA as the main algorithm with the support of a triangular algorithm in face normalization in order to enhance indexing accuracy. To evaluate the accuracy of the proposed hybrid indexing algorithm, the PCAaTA is tested and the results are compared with current solutions.

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Moghaddasi, Zahra, Hamid A. Jalab, Rafidah Md Noor, and Saeed Aghabozorgi. "Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/606570.

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Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.

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Trivedi, Gargi, and Dr Rajesh Sanghvi. "Optimizing Image Fusion Using Modified Principal Component Analysis Algorithm and Adaptive Weighting Scheme." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5769–74. http://dx.doi.org/10.35444/ijana.2023.15103.

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Image fusion is an important technique for combining two or more images to produce a single, high-quality image. Principal component analysis (PCA) is a commonly used method for image fusion. However, existing PCA-based image fusion algorithms have some limitations, such as sensitivity to noise and poor fusion quality. In this paper, we propose a modified PCA algorithm for image fusion that uses an adaptive weighting scheme to improve the fusion quality. The proposed algorithm optimizes the fusion process by selecting the principal components that contain the most useful information and weighing them appropriately. Experimental results show that the proposed algorithm outperforms existing PCA-based image fusion algorithms in terms of fusion quality, sharpness, and contrast. Keywords - Image fusion, principle components analysis, adaptive weighting scheme, optimization, fusion quality, sharpness,contrast.

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Zhaxi, Cai Rang, and Yue Guang Li. "A Novel Face Recognition Algorithm." Advanced Materials Research 718-720 (July 2013): 2055–61. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2055.

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This paper firstly analyzes the principle of face recognition algorithm, studies feature selection and distance criterion problem, puts forward the defects of PCA face recognition algorithm and LDA face recognition algorithm. According to the deficiencies and shortcomings of PCA face recognition algorithm and LDA face recognition algorithm, this paper proposes a solution -- PCA+LDA. The method uses the PCA method to reduce the dimensionality of feature space, it uses Fisher linear discriminant analysis method to classification, the realization of face recognition. Experiments show that, this method can not only improve the feature extraction speed, but also the recognition rate is better than single PCA method and LDA method.

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Patil, Shweta, and S. S. Katariya. "Facial Expression Recognition using PCA Algorithm." Communications on Applied Electronics 3, no. 3 (October 23, 2015): 22–24. http://dx.doi.org/10.5120/cae2015651904.

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Schmitt, Eric, and Kaveh Vakili. "The FastHCS algorithm for robust PCA." Statistics and Computing 26, no. 6 (October 8, 2015): 1229–42. http://dx.doi.org/10.1007/s11222-015-9602-5.

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Wang, Yang, and Qiang Wu. "Sparse PCA by iterative elimination algorithm." Advances in Computational Mathematics 36, no. 1 (September 22, 2011): 137–51. http://dx.doi.org/10.1007/s10444-011-9186-3.

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Huang, Dong, Zhang Yi, and Xiaorong Pu. "A new local PCA-SOM algorithm." Neurocomputing 71, no. 16-18 (October 2008): 3544–52. http://dx.doi.org/10.1016/j.neucom.2007.10.004.

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Adamu, Nuraddeen, Samaila Abdullahi, and Sani Musa. "Online Stochastic Principal Component Analysis." Caliphate Journal of Science and Technology 4, no. 1 (February 10, 2022): 101–8. http://dx.doi.org/10.4314/cajost.v4i1.13.

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This paper studied Principal Component Analysis (PCA) in an online. The problem is posed as a subspace optimization problem and solved using gradient based algorithms. One such algorithm is the Variance-Reduced PCA (VR-PCA). The VR-PCA was designed as an improvement to the classical online PCA algorithm known as the Oja’s method where it only handled one sample at a time. The paper developed Block VR-PCA as an improved version of VR-PCA. Unlike prominent VR-PCA, the Block VR-PCA was designed to handle more than one dimension in subspace optimization at a time and it showed good performance. The Block VR-PCA and Block Oja method were compared experimentally in MATLAB using synthetic and real data sets, their convergence results showed Block VR-PCA method appeared to achieve a minimum steady state error than Block Oja method. Keywords: Online Stochastic; Principal Component Analysis; Block Variance-Reduced; Block Oja

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Lasarev, Michael R., Eric R. Bialk, David B. Allen, and Patrice K. Held. "Application of Principal Component Analysis to Newborn Screening for Congenital Adrenal Hyperplasia." Journal of Clinical Endocrinology & Metabolism 105, no. 8 (June 11, 2020): e2930-e2940. http://dx.doi.org/10.1210/clinem/dgaa371.

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Abstract Purpose Newborn screening laboratories are challenged to develop reporting algorithms that accurately identify babies at increased risk for congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency (21OHD). Screening algorithms typically use cutoff values for a key steroid(s) and include considerations for covariates, such as gestational age or birth weight, but false-positive and false-negative results are still too frequent, preventing accurate assessments. Principal component analysis (PCA) is a statistical method that reduces high-dimensional data to a small number of components, capturing patterns of association that may be relevant to the outcome of interest. To our knowledge, PCA has not been evaluated in the newborn screening setting to determine whether it can improve the positive predictive value of 21OHD screening. Methods PCA was applied to a data set of 920 newborns with measured concentrations of 5 key steroids that are known to be perturbed in patients with 21OHD. A decision tree for the known outcomes (confirmed 21OHD cases and unaffected individuals) was created with 2 principal components as predictors. The effectiveness of the PCA-derived decision tree was compared with the current algorithm. Results PCA improved the positive predictive value of 21OHD screening from 20.0% to 66.7% in a retrospective study comparing the current algorithm to a tree-based algorithm using PCA-derived variables. The streamlined PCA-derived decision tree, comprising only 3 assessment points, greatly simplified the 21OHD reporting algorithm. Conclusions This first report of PCA applied to newborn screening for 21OHD demonstrates enhanced detection of affected individuals within the unaffected population.

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Xu, Ke. "Application of portrait recognition system for emergency evacuation in mass emergencies." Journal of Intelligent Systems 30, no. 1 (January 1, 2021): 893–902. http://dx.doi.org/10.1515/jisys-2021-0052.

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Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

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Wang, Zhenyi, Yalei Wang, and Xiaoliang Jin. "Prediction of Grade Classification of Rock Burst Based on PCA-SSA-PNN Architecture." Geofluids 2023 (June 13, 2023): 1–11. http://dx.doi.org/10.1155/2023/5299919.

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The uncertainty and complexity of rock burst brings great difficulties to the prediction of rock burst grades. In order to estimate the risk grades of rock burst, an integrated method combining principal component analysis (PCA) and sparrow search algorithm (SSA) with probabilistic neural network (PNN) was proposed. Considering that the in situ stress of rock mass, the strength of rock, and the strength of rock mass are the key influencing factors of rock bursts, the maximum in situ stress σ max , maximum tangential stress σ θ , rock strength σ ci , rock mass strength σ cm , and three rock burst evaluation indexes ( σ θ / σ ci , σ ci / σ max , and σ cm / σ max ) were selected to constitute the rock burst grade evaluation index system. Forty-three groups of rock burst engineering data were gathered. After preprocessing the rock burst data using PCA, four of the new linearly independent indexes PCA1, PCA2, PCA3, and PCA4 were obtained for estimating rock burst grades. The SSA was utilized to optimize the smoothing factor in the PNN. Using PCA-SSA-PNN-based architecture, a new multi-index rock burst grade prediction method was proposed. The results from the new multi-index rock burst grade prediction method were compared with those from single- and multi-index prediction methods. It shows that the predictions from the multi-index rock burst prediction methods are closer to the actual rock burst grades than that from the single-index rock burst prediction methods; compared with other multi-index rock burst prediction methods, the prediction accuracy of PCA-SSA-PNN is greater (up to 90%) and more available in the prediction of rock burst grades. The results presented herein may provide reference for the rock burst warning.

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Guojiang, Wang, and Yang Guoliang. "Facial Expression Recognition Using PCA and AdaBoost Algorithm." International Journal of Signal Processing Systems 7, no. 2 (March 2019): 73–77. http://dx.doi.org/10.18178/ijsps.7.2.73-77.

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Ishida, Emille E. O. "Kernel PCA for Supernovae Photometric Classification." Proceedings of the International Astronomical Union 10, H16 (August 2012): 683–84. http://dx.doi.org/10.1017/s1743921314012897.

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AbstractIn this work, we propose the use of Kernel Principal Component Analysis (KPCA) combined with k = 1 nearest neighbor algorithm (1NN) as a framework for supernovae (SNe) photometric classification. It is specially recommended for analysis where the user is interested in high purity in the final SNe Ia sample. Our method provide good purity results in all data sample analyzed, when SNR⩾5. As a consequence, we can state that if a sample as the Supernova Photometric Classification Challenge were available today, we would be able to classify ≈ 15% of the initial data set with purity higher than 90%. This makes our algorithm ideal for a first approach to an unlabeled data set or to be used as a complement in increasing the training sample for other algorithms. Results are sensitive to the information contained in each light curve, as a consequence, higher quality data (low noise) leads to higher successful classification rates.

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Luo, Rui, Qingxiang Zeng, and Huashan Chen. "Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer." Computational and Mathematical Methods in Medicine 2022 (June 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/8123643.

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The rapid increase in prostate cancer (PCa) patients is similar to that of benign prostatic hyperplasia (BPH) patients, but the treatments are quite different. In this research, magnetic resonance imaging (MRI) images under the weighted low-rank matrix restoration algorithm (RLRE) were utilized to differentiate PCa from BPH. The diagnostic effects of different sequences of MRI images were evaluated to provide a more effective examination method for the clinical differential diagnosis of PCa and BPH. 150 patients with suspected PCa were taken as the research objects. Pathological examination revealed that 137 patients had PCa and 13 patients had BPH. The pathological results were the gold standard and were compared with the MRI results of different sequences. Therefore, the accuracy of the MRI results was evaluated. The results showed that with the rise of Gaussian noise, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of all three algorithms gradually decreased, but the PSNR and SSIM of the RLRE algorithm were always higher than those of the RL and BM3D algorithms ( P < 0.05 ). The sensitivity (97.08%), specificity (92.31%), accuracy (96.67%), and consistency (0.678) of the dynamic contrast enhancement (DCE) sequence were higher than those of the plain scan (86.13%, 69.23%, 84.67%, and 0.469, respectively). In conclusion, the RLRE algorithm could promote the resolution of MRI images and improve the display effect. DCE could better differentiate PCa from BPH, had great clinical application value, and was worthy of clinical promotion.

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Albitar, Maher, Wanlong Ma, Kevin Diep, Ferras S. Albitar, Herbert A. Fritsche, and Neal D. Shore. "Using a combination of urine and plasma biomarkers for the development of a scoring sytem that can diagnose and predict prognosis of prostate cancer." Journal of Clinical Oncology 32, no. 4_suppl (February 1, 2014): 163. http://dx.doi.org/10.1200/jco.2014.32.4_suppl.163.

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163 Background: Relying solely on serum prostate-specific antigen (sPSA) to screen for prostate cancer (PCa) can lead to unnecessary biopsies. Biomarkers from urine and plasma were isolated to develop a detection scoring system for the presence of prostate cancer as well as to better predict aggressiveness. Methods: Urine and plasma specimens were analyzed from 141 patients (61 newly diagnosed PCa patients, 60 benign prostate hyperplasia (BPH) patients, and 20 post-prostatectomy patients) using polymerase chain reaction (PCR) for the levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, and B2M genes. Patient age, sPSA level, and PCR data were entered through multiple algorithms to determine models most useful for detection of cancer and predicting aggressiveness. Results: We developed an algorithm for distinguishing PCa from BPH (area under the receiver operating characteristic curve [AUROC] of 0.78). Another algorithm distinguishes patients with Gleason Score (GS) ≥ 7 from GS < 7 cancer or BPH (AUROC of 0.88). By incorporating the two algorithms into a scoring system, 75% of the analyzed samples showed concordance between the two models (99% specificity and 68% sensitivity for predicting GS ≥ 7), and 25% showed discordance. Conclusions: A scoring system incorporating two algorithms using urine and plasma biomarkers highly predicts the presence of GS ≥ 7 PCa in 75% of patients. In 25% of patients, the system can be used only to distinguish between the presence of cancer and benign pathology. Our algorithms may assist with both biopsy indication as well as patient prognosis. [Table: see text]

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Yi, Ting-Hua, Kai-Fang Wen, and Hong-Nan Li. "A new swarm intelligent optimization algorithm: Pigeon Colony Algorithm (PCA)." Smart Structures and Systems 18, no. 3 (September 25, 2016): 425–48. http://dx.doi.org/10.12989/sss.2016.18.3.425.

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He, Shi Jun, Shi Ting Zhao, Fan Bai, and Jia Wei. "A Method for Spatial Data Registration Based on PCA-ICP Algorithm." Advanced Materials Research 718-720 (July 2013): 1033–36. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1033.

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The spatial data which acquired by 3D laser scanning is huge, aiming at the iteration time is long with classic ICP algorithm, a improved registration algorithm of spatial data ICP algorithm which based on principal component analysis (PCA) is proposed in this paper (PCA-ICP), the basic principle and steps of PCA-ICP algorithm are given. The experiment results show that this method is feasible and the iterative time of PCA-ICP algorithm is shorter than classical ICP algorithm.

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Karhunen, Juha. "Stability of Oja's PCA Subspace Rule." Neural Computation 6, no. 4 (July 1994): 739–47. http://dx.doi.org/10.1162/neco.1994.6.4.739.

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This paper deals with stability of Oja's symmetric algorithm for estimating the principal component subspace of the input data. Exact conditions are derived for the gain parameter on which the discrete algorithm remains bounded. The result is extended for a nonlinear version of Oja's algorithm.

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Asiedu, Louis, Felix O. Mettle, and Joseph A. Mensah. "Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm." Journal of Applied Mathematics 2020 (October 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/9127465.

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Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample t-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.

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JANKOVIC, MARKO, and HIDEMITSU OGAWA. "TIME-ORIENTED HIERARCHICAL METHOD FOR COMPUTATION OF PRINCIPAL COMPONENTS USING SUBSPACE LEARNING ALGORITHM." International Journal of Neural Systems 14, no. 05 (October 2004): 313–23. http://dx.doi.org/10.1142/s0129065704002091.

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Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms — Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfilment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

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Juneja, Jyoti, and Avani Chopra. "GLCM and PCA Algorithm based Watermarking Scheme." International Journal of Computer Applications 180, no. 48 (June 15, 2018): 24–29. http://dx.doi.org/10.5120/ijca2018917261.

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Akram, Noreen, and Naeem Abbas. "Automated Facial Expression System using PCA Algorithm." International Journal of Computer Applications 182, no. 9 (August 14, 2018): 32–36. http://dx.doi.org/10.5120/ijca2018917681.

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Gómez-Pedrero, José A., Julio C. Estrada, Jose Alonso, Juan A. Quiroga, and Javier Vargas. "Incremental PCA algorithm for fringe pattern demodulation." Optics Express 30, no. 8 (March 28, 2022): 12278. http://dx.doi.org/10.1364/oe.452463.

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Fountoulakis, Kimon, Abhisek Kundu, Eugenia-Maria Kontopoulou, and Petros Drineas. "A Randomized Rounding Algorithm for Sparse PCA." ACM Transactions on Knowledge Discovery from Data 11, no. 3 (April 14, 2017): 1–26. http://dx.doi.org/10.1145/3046948.

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Chen, Bei, and Kun Song. "Affinity Propagation Clustering Algorithm Based on PCA." Applied Mechanics and Materials 590 (June 2014): 688–92. http://dx.doi.org/10.4028/www.scientific.net/amm.590.688.

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Overlap information usually exits in the high-dimensional data. Misclassified points may be more when affinity propagation clustering is applied to these data. Concerning this problem, a new method combining principal components analysis and affinity propagation clustering is proposed. In this method, dimensionality of the original data is reduced on the premise of reserving most information of the variables. Then, affinity propagation clustering is implemented in the low-dimensional space. Thus, because the redundant information is deleted, the classification is accurate. Experiment is done by using this new method, the results of the experiment explain that this method is effective.

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Neumayer, Sebastian, Max Nimmer, Simon Setzer, and Gabriele Steidl. "On the Robust PCA and Weiszfeld’s Algorithm." Applied Mathematics & Optimization 82, no. 3 (April 5, 2019): 1017–48. http://dx.doi.org/10.1007/s00245-019-09566-1.

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Jiawen, Li, and Li Congxin. "Information criterion based fast PCA adaptive algorithm." Journal of Systems Engineering and Electronics 18, no. 2 (June 2007): 377–84. http://dx.doi.org/10.1016/s1004-4132(07)60101-7.

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Hameed Al-Hamdani, Maysaa. "Face Image Recognition Using 2D PCA Algorithm." Engineering and Technology Journal 31, no. 7A (June 1, 2013): 1404–17. http://dx.doi.org/10.30684/etj.31.7a13.

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Li, Qiao Yan, and Hai Yan Quan. "The Dimension Reduction Method of Face Feature Parameters Based on Modular 2DPCA and PCA." Applied Mechanics and Materials 687-691 (November 2014): 4037–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4037.

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In face recognition algorithms, Principal Component Analysis (PCA) is one of classical algorithms. But PCA algorithm needs to convert each sample matrix into vectors, which leads to a large amount of calculations in solving high-rank matrix. The essence of Modular Two-dimensional Principle Component Analysis (2DPCA) is that original images are divided into modular images, and image covariance matrix is constructed directly from the sub-images by the optimal projection matrix. But the number of features is still large and correlation still exists in feature extraction, which influences the speed of classification. In order to solve this problem, we proposed a method combining the Modular 2DPCA with PCA to reduce the dimension of features and decrease the correlation among feature parameters. The experimental results based on ORL Human Face Database show that the recognition rate of the algorithm is superior to single Modular 2DPCA or PCA.

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Karuna, Yepuganti, Saritha Saladi, and Budhaditya Bhattacharyya. "Brain Tissue Classification using PCA with Hybrid Clustering Algorithms." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 536. http://dx.doi.org/10.14419/ijet.v7i2.24.12155.

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Distinct algorithms were developed to segment the MRI images, to satisfy the accuracy in segmenting the regions of the brain. In this paper, we proposed a novel methodology for segmenting the MRI brain images using the clustering techniques. The Modified Fuzzy C-Means (MFCM) algorithm is pooled with the Artificial Bee Colony (ABC) algorithm after denoising images, features are extracted using Principal Component Analysis (PCA) for better results of segmentation. This improves the ability to extract the regions (cluster centres) and cells in the normal and abnormal brain MRI images. The comparative analysis of proposed methodology with existing FCM, ABC algorithms is evaluated in terms of Minkowski score. The proposed MFCM-ABC method is more robust and efficient to hostile noise in images when compared to existing FCM and ABC methods.

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Zeng, Qingxi, Wenqi Qiu, Pengna Zhang, Xuefen Zhu, and Ling Pei. "A Fast Acquisition Algorithm Based on Division of GNSS Signals." Journal of Navigation 71, no. 4 (February 2, 2018): 933–54. http://dx.doi.org/10.1017/s0373463317000984.

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The acquisition of signals is a precondition for tracking and solution calculation in software-based Global Navigation Satellite System (GNSS) receivers. The Parallel Code phase Acquisition (PCA) algorithm can simultaneously obtain the correlation results at every sampling point. However, if the number of sampling points that needs processing is large, this method will lead to a heavy computational load. Thus, we improve the process of the PCA algorithm and propose a novel algorithm that divides the signals intoK(Kis a constant) parts to achieve correlation and obtains the correlation results with a fusion algorithm. This algorithm can simultaneously obtain the correlation results for sampling points at an interval ofKpoints. If theKvalue is selected appropriately, the computational load can be decreased by about 50%. Also, the Receiver Operating Characteristic (ROC) curves show that under a certain probability of false alarm, the detection probability of the proposed algorithms is 5% lower than that of the PCA algorithm. Therefore, the proposed algorithm can speed up the acquisition process with a slight decrease in detection probability.

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Yang, Wenkao, Jing Wang, and Jing Guo. "A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/708985.

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This paper studies the image fusion of high-resolution panchromatic image and low-resolution multispectral image. Based on the classic fusion algorithms on remote sensing image fusion, the PCA (principal component analysis) transform, and discrete wavelet transform, we carry out in-depth research. The compressed sensing (CS) abandons the full sample and shifts the sampling of the signal to sampling information that greatly reduces the potential consumption of traditional signal acquisition and processing. We combine compressed sensing with satellite remote sensing image fusion algorithm and propose an innovative fusion algorithm (CS-FWT-PCA), in which the symmetric fractional B-spline wavelet acts as the sparse base. In the algorithm we use Hama Da matrix as the measurement matrix and SAMP as the reconstruction algorithm and adopt an improved fusion rule based on the local variance. The simulation results show that the CS-FWT-PCA fusion algorithm achieves better fusion effect than the traditional fusion method.

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Gore, Pritee. "Identification and Detection of Sugarcane Crop Disease Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 378–80. http://dx.doi.org/10.22214/ijraset.2021.36635.

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Sugarcane is a renewable, natural agriculture resource and it is most important crop of India. Sugarcane Crop is a perennial crop which results into less labour and high yields. Sugarcane crop is one of the main pillar for Indian economy. Nowadays there are different diseases which affecting the sugarcane plants in diverse areas. So In this work we are going to use machine learning algorithms and image processing for sugarcane leaf disease detection. Machine learning is a trending area where the technological benefits can be imparted to the agriculture field also. In this we are going to use PCA algorithm which is one of the unsupervised machine learning algorithms. The dataset consists of 3 types of diseases. Total dataset is divided into various proportions of training and testing sets. There are various detection and classification techniques which are done using various algorithms at each stage but in PCA algorithm detection and classification is done by same algorithm which is PCA. The diseases of sugarcane consider in this project are red rot, smut, wilt.

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Zhang, Hongyang, Zhouchen Lin, Chao Zhang, and Junbin Gao. "Relations Among Some Low-Rank Subspace Recovery Models." Neural Computation 27, no. 9 (September 2015): 1915–50. http://dx.doi.org/10.1162/neco_a_00762.

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Recovering intrinsic low-dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, a lot of work has modeled subspace recovery as low-rank minimization problems. We find that some representative models, such as robust principal component analysis (R-PCA), robust low-rank representation (R-LRR), and robust latent low-rank representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low-rank subspace recovery models. Our work has two important implications. First, R-PCA has a solid theoretical foundation. Under certain conditions, we could find globally optimal solutions to these low-rank models at an overwhelming probability, although these models are nonconvex. Second, we can obtain significantly faster algorithms for these models by solving R-PCA first. The computation cost can be further cut by applying low-complexity randomized algorithms, for example, our novel [Formula: see text] filtering algorithm, to R-PCA. Although for the moment the formal proof of our [Formula: see text] filtering algorithm is not yet available, experiments verify the advantages of our algorithm over other state-of-the-art methods based on the alternating direction method.

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Ning, Xiao Mei, and Yan Shi. "Research and Implementation on Face Recognition Algorithm." Applied Mechanics and Materials 151 (January 2012): 657–60. http://dx.doi.org/10.4028/www.scientific.net/amm.151.657.

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This paper used the PCA based method to build a face recognition system. At first, PAC provided a linear transformation matrix between the high and low dimensional spaces, then multi-dimensional Euclidean distance were used to rebuild residual to reduce the number of the obtained dimensions, speed up and high accuracy, which simplified the process of face recognition. Simulation results proved this algorithm is efficient.

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Huang, Ke Wang. "Experimental Study of FPCA on its Generalization Performance in Image Classification." Applied Mechanics and Materials 496-500 (January 2014): 2299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2299.

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The theoretical study of FPCA shows that FPCA algorithm has better generalization performance than existing PCA and its extended algorithms. But this theoretic conclusion was not confirmed by existing experimental results because of the problems of evaluation criterion. Introducing the idea of clustering performance criterion of LDA, we proposed a general performance metrics for PCA and performed numbers of experimental studies to compare FPCA with existing PCA and its extended algorithms by using our metrics. We found in the feature extraction of image samples that FPCA really has better generalization performance than existing PCA and its extended algorithms under the condition of large sample size. The results confirmed theoretical conclusion of FPCA and improved relevant experimental study.

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Abdel-Qader, Ikhlas, Lixin Shen, Christina Jacobs, Fadi Abu Amara, and Sarah Pashaie-Rad. "Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA." International Journal of Biomedical Imaging 2006 (2006): 1–11. http://dx.doi.org/10.1155/ijbi/2006/57850.

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Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database.

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Wen, Jing, and Nai Zhong Zhang. "Hand Posture Recognition Based on PCA." Applied Mechanics and Materials 738-739 (March 2015): 631–34. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.631.

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In this paper, we present an algorithm which detects human hand by skin color information in YCbCr and HIS color model. And for confirming special human hand we use circle rate of region to detect hand region because human hand have complex edge than other region, thus circle rate of hand region is usually more greater. For the recognition of detected hand, we use the Hausdorff to tracking the hand region. And we employed a recognition method based on PCA algorithm to recognize the hand gestures. The experimental results show that an algorithm plays an efficient effort for hand gesture recognition.

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Maafiri, Ayyad, and Khalid Chougdali. "Robust face recognition based on a new Kernel-PCA using RRQR factorization." Intelligent Data Analysis 25, no. 5 (September 15, 2021): 1233–45. http://dx.doi.org/10.3233/ida-205377.

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In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rate of RRQR PCA by developing a nonlinear extension of RRQR PCA. In addition, a new robust RBF Lp-norm kernel is proposed in order to reduce the effect of outliers and noises. Extensive experiments on two well-known standard face databases which are ORL and FERET prove that the proposed algorithm is more robust than conventional PCA, 2DPCA, PCA-L1, WTPCA-L1, LDA, and 2DLDA in terms of face recognition accuracy.

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Yu, Cheng Bo, Jun Tan, Lei Yu, and Yin Li Tian. "A Finger Vein Recognition Method Based on PCA-RBF Neural Network." Applied Mechanics and Materials 325-326 (June 2013): 1653–58. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1653.

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This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

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Zhang, Ning Li, Yan Ma, Xiang Fen Zhang, and Yan Lu Xu. "An Improved PCA-SIFT Algorithm by Fuzzy K-Means for Image Matching." Applied Mechanics and Materials 644-650 (September 2014): 4291–96. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4291.

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Image matching plays an important role in computer vision. The features extracted by SIFT algorithm have high stability invariant to scale, rotation and light, so it is the most popular algorithm for image matching. While SIFT algorithm also has its disadvantages of high dimensional data and time-consuming. To solve this problem, the traditional method employs PCA algorithm to reduce dimensionality of the descriptors. While PCA is a linear dimensionality reduction algorithm which means that it can only be used for linear distributed data. This paper employs the fuzzy K-means algorithm to improve it (referred to as FKPCA) and improved RANSAC algorithm to eliminate false matching points after matching with PCA-SIFT and FKPCA-SIFT. From the experimental results, compared with PCA-SIFT algorithm, it can be seen that FKPCA-SIFT can keep the high matching accuracy for image. Moreover, FKPCA-SIFT can also be applied to non-linear data to expand the scope of PCA-SIFT and provides a better reference platform for further research.

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Journal articles on the topic 'PCA ALGORITHM'

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