Relevant bibliographies by topics / Out-of-sample Embedding

Academic literature on the topic 'Out-of-sample Embedding'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Out-of-sample Embedding"

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Wang, Jianzhong. "Mathematical analysis on out-of-sample extensions." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (September 2018): 1850042. http://dx.doi.org/10.1142/s021969131850042x.

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Let [Formula: see text] be a data set in [Formula: see text], where [Formula: see text] is the training set and [Formula: see text] is the test one. Many unsupervised learning algorithms based on kernel methods have been developed to provide dimensionality reduction (DR) embedding for a given training set [Formula: see text] ([Formula: see text]) that maps the high-dimensional data [Formula: see text] to its low-dimensional feature representation [Formula: see text]. However, these algorithms do not straightforwardly produce DR of the test set [Formula: see text]. An out-of-sample extension method provides DR of [Formula: see text] using an extension of the existent embedding [Formula: see text], instead of re-computing the DR embedding for the whole set [Formula: see text]. Among various out-of-sample DR extension methods, those based on Nyström approximation are very attractive. Many papers have developed such out-of-extension algorithms and shown their validity by numerical experiments. However, the mathematical theory for the DR extension still need further consideration. Utilizing the reproducing kernel Hilbert space (RKHS) theory, this paper develops a preliminary mathematical analysis on the out-of-sample DR extension operators. It treats an out-of-sample DR extension operator as an extension of the identity on the RKHS defined on [Formula: see text]. Then the Nyström-type DR extension turns out to be an orthogonal projection. In the paper, we also present the conditions for the exact DR extension and give the estimate for the error of the extension.
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Strange, Harry, and Reyer Zwiggelaar. "A Generalised Solution to the Out-of-Sample Extension Problem in Manifold Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 471–76. http://dx.doi.org/10.1609/aaai.v25i1.7908.

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Manifold learning is a powerful tool for reducing the dimensionality of a dataset by finding a low-dimensional embedding that retains important geometric and topological features. In many applications it is desirable to add new samples to a previously learnt embedding, this process of adding new samples is known as the out-of-sample extension problem. Since many manifold learning algorithms do not naturally allow for new samples to be added we present an easy to implement generalized solution to the problem that can be used with any existing manifold learning algorithm. Our algorithm is based on simple geometric intuition about the local structure of a manifold and our results show that it can be effectively used to add new samples to a previously learnt embedding. We test our algorithm on both artificial and real world image data and show that our method significantly out performs existing out-of-sample extension strategies.
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Fanuel, Michaël, Antoine Aspeel, Jean-Charles Delvenne, and Johan A. K. Suykens. "Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions." SIAM Journal on Mathematics of Data Science 4, no. 1 (February 10, 2022): 153–78. http://dx.doi.org/10.1137/20m1370653.

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Bengio, Yoshua, Olivier Delalleau, Nicolas Le Roux, Jean-François Paiement, Pascal Vincent, and Marie Ouimet. "Learning Eigenfunctions Links Spectral Embedding and Kernel PCA." Neural Computation 16, no. 10 (October 1, 2004): 2197–219. http://dx.doi.org/10.1162/0899766041732396.

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In this letter, we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density. Whereas spectral embedding methods provided only coordinates for the training points, the analysis justifies a simple extension to out-of-sample examples (the Nyström formula) for multidimensional scaling (MDS), spectral clustering, Laplacian eigenmaps, locally linear embedding (LLE), and Isomap. The analysis provides, for all such spectral embedding methods, the definition of a loss function, whose empirical average is minimized by the traditional algorithms. The asymptotic expected value of that loss defines a generalization performance and clarifies what these algorithms are trying to learn. Experiments with LLE, Isomap, spectral clustering, and MDS show that this out-of-sample embedding formula generalizes well, with a level of error comparable to the effect of small perturbations of the training set on the embedding.
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Yang, Yi, Feiping Nie, Shiming Xiang, Yueting Zhuang, and Wenhua Wang. "Local and Global Regressive Mapping for Manifold Learning with Out-of-Sample Extrapolation." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 649–54. http://dx.doi.org/10.1609/aaai.v24i1.7696.

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Over the past few years, a large family of manifold learning algorithms have been proposed, and applied to various applications. While designing new manifold learning algorithms has attracted much research attention, fewer research efforts have been focused on out-of-sample extrapolation of learned manifold. In this paper, we propose a novel algorithm of manifold learning. The proposed algorithm, namely Local and Global Regressive Mapping (LGRM), employs local regression models to grasp the manifold structure. We additionally impose a global regression term as regularization to learn a model for out-of-sample data extrapolation. Based on the algorithm, we propose a new manifold learning framework. Our framework can be applied to any manifold learning algorithms to simultaneously learn the low dimensional embedding of the training data and a model which provides explicit mapping of the out-of-sample data to the learned manifold. Experiments demonstrate that the proposed framework uncover the manifold structure precisely and can be freely applied to unseen data.
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Tong, Ying, Jiachao Zhang, and Rui Chen. "Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition." Electronics 8, no. 5 (May 7, 2019): 503. http://dx.doi.org/10.3390/electronics8050503.

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In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influence between the given sample and the pseudo-neighbor samples with the different classes. Then, the global distribution constraints are introduced to the projection objective function for seeking the optimal subspace which compacts intra-classes samples and alienates inter-classes samples at the same time. Extensive experiments are carried out on AR, Extended Yale B, LFW and PubFig databases which are four representative face datasets, and the corresponding experimental results illustrate the effectiveness of our proposed method.
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Liu, Bing, Shixiong Xia, and Yong Zhou. "Multiple Kernel Spectral Regression for Dimensionality Reduction." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/427462.

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Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR) solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL) into SR for dimensionality reduction. The proposed approach (termed MKL-SR) seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS) induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR) further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
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DABBAGH, LANJA A., and Ismael M. Saeed. "Narrative Embedding in The Postmodern American Novel with Reference to Stephen King’s Desperation." Humanities Journal of University of Zakho 1, no. 1 (June 30, 2013): 7–11. http://dx.doi.org/10.26436/hjuoz.2013.1.1.87.

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The inclusion of a story within another story is a device which implies that there is an embedding of at least an additional narrative subordinated to the one that seems to frame it. This invariably signifies the shift back and forth of enunciative levels and/or text-world dimensions. Stephen King’s Desperation (1996) is the American postmodern novel which embodies the multiple framing of the fictional discourse by including some stretches and story-telling accounts based on chronicles, reproduced in chapter three, part III of the said novel. The embedded narrative is also given an autonomous subtitle “The American West: Legendary Shadows”, dealing with how the Chinese mine workers were treated in 1858-1859, while the novel discourse narrates events that took place in the second half of the twentieth century. The paper provides a sample analysis of the embeddings to reveal the intricate connection between the text-world polarities, its multiverse interaction, and the narrator’s/narrators’ presentational modes. Stephen King, as the inventor of the narrative levels, works out a complex set of diegesis vs. mimesis, as well as management of reporting distance in the representation of polyphony, voice, and voice-related issues in the embeddings. Despite the complexity and idiosyncracies of the structure, Desperation manages to attract the reader’s attention to the end, owing to the symbiotic relations between the frame and the embedding; and, moreover, owing to the interchangeable positions of these two in the course of the events, as will be proved in the paper.
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Chen, Xu, Xiaoli Qi, Zhenya Wang, Chuangchuang Cui, Baolin Wu, and Yan Yang. "Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding." Measurement 176 (May 2021): 109116. http://dx.doi.org/10.1016/j.measurement.2021.109116.

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SOOFI, ABDOL S., and LIANGYUE CAO. "PREDICTION AND VOLATILITY OF BLACK MARKET CURRENCIES: EVIDENCE FROM RENMINBI AND RIAL EXCHANGE RATES." International Journal of Theoretical and Applied Finance 05, no. 06 (September 2002): 659–66. http://dx.doi.org/10.1142/s0219024902001638.

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We perform out-of-sample prediction on both fixed and black market Chinese renminbi/US dollar, and black market rial/US dollar exchange rates by using the time-delay embedding technique and the local linear prediction method. We also predict an artificially generated chaotic time series with and without noise for the purpose of validation of the methods used in this study. In all examples tested, our prediction results significantly outperform those by the benchmark mean value predictor based on a statistic defined by Harvey et al. [11]. Another interesting result found in this paper is that one may use the embedding dimension as a measure of volatility of a financial asset.
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Dissertations / Theses on the topic "Out-of-sample Embedding"

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Herath, Samudra Dilrukshi. "Embedding Techniques to Solve Large-scale Entity Resolution." Thesis, 2022. https://hdl.handle.net/2440/135396.

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Entity resolution (ER) identifies and links records that belong to the same real-world entities, where an entity refer to any real-world object. It is a primary task in data integration. Accurate and efficient ER substantially impacts various commercial, security, and scientific applications. Often, there are no unique identifiers for entities in datasets/databases that would make the ER task easy. Therefore record matching depends on entity identifying attributes and approximate matching techniques. The issues of efficiently handling large-scale data remain an open research problem with the increasing volumes and velocities in modern data collections. Fast, scalable, real-time and approximate entity matching techniques that provide high-quality results are highly demanding. This thesis proposes solutions to address the challenges of lack of test datasets and the demand for fast indexing algorithms in large-scale ER. The shortage of large-scale, real-world datasets with ground truth is a primary concern in developing and testing new ER algorithms. Usually, for many datasets, there is no information on the ground truth or ‘gold standard’ data that specifies if two records correspond to the same entity or not. Moreover, obtaining test data for ER algorithms that use personal identifying keys (e.g., names, addresses) is difficult due to privacy and confidentiality issues. Towards this challenge, we proposed a numerical simulation model that produces realistic large-scale data to test new methods when suitable public datasets are unavailable. One of the important findings of this work is the approximation of vectors that represent entity identification keys and their relationships, e.g., dissimilarities and errors. Indexing techniques reduce the search space and execution time in the ER process. Based on the ideas of the approximate vectors of entity identification keys, we proposed a fast indexing technique (Em-K indexing) suitable for real-time, approximate entity matching in large-scale ER. Our Em-K indexing method provides a quick and accurate block of candidate matches for a querying record by searching an existing reference database. All our solutions are metric-based. We transform metric or non-metric spaces to a lowerdimensional Euclidean space, known as configuration space, using multidimensional scaling (MDS). This thesis discusses how to modify MDS algorithms to solve various ER problems efficiently. We proposed highly efficient and scalable approximation methods that extend the MDS algorithm for large-scale datasets. We empirically demonstrate the improvements of our proposed approaches on several datasets with various parameter settings. The outcomes show that our methods can generate large-scale testing data, perform fast real-time and approximate entity matching, and effectively scale up the mapping capacity of MDS.
Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2022
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Book chapters on the topic "Out-of-sample Embedding"

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Raducanu, Bogdan, and Fadi Dornaika. "Out-of-Sample Embedding by Sparse Representation." In Lecture Notes in Computer Science, 336–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_37.

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Zhang, Yongpeng, Zenggang Lin, Rui Yao, Yu Zhu, and Haisen Li. "Out-of-Sample Embedding of Spherical Manifold Based on Constrained Least Squares." In Intelligent Science and Intelligent Data Engineering, 562–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_72.

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Barbaglia, Luca, Sergio Consoli, and Sebastiano Manzan. "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting." In Mining Data for Financial Applications, 135–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_11.

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AbstractForecasting economic and financial variables is a challenging task for several reasons, such as the low signal-to-noise ratio, regime changes, and the effect of volatility among others. A recent trend is to extract information from news as an additional source to forecast economic activity and financial variables. The goal is to evaluate if news can improve forecasts from standard methods that usually are not well-specified and have poor out-of-sample performance. In a currently on-going project, our goal is to combine a richer information set that includes news with a state-of-the-art machine learning model. In particular, we leverage on two recent advances in Data Science, specifically on Word Embedding and Deep Learning models, which have recently attracted extensive attention in many scientific fields. We believe that by combining the two methodologies, effective solutions can be built to improve the prediction accuracy for economic and financial time series. In this preliminary contribution, we provide an overview of the methodology under development and some initial empirical findings. The forecasting model is based on DeepAR, an auto-regressive probabilistic Recurrent Neural Network model, that is combined with GloVe Word Embeddings extracted from economic news. The target variable is the spread between the US 10-Year Treasury Constant Maturity and the 3-Month Treasury Constant Maturity (T10Y3M). The DeepAR model is trained on a large number of related GloVe Word Embedding time series, and employed to produce point and density forecasts.
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Nechval, N. A., and K. N. Nechval. "Effective Optimization of Statistical Decisions for Age Replacement Problems under Parametric Uncertainty." In Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, 1–16. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1639-2.ch001.

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In this chapter, an innovative model for age replacement is proposed. The costs included in the age replacement model are not assumed to be constants. For effective optimization of statistical decisions for age replacement problems under parametric uncertainty, based on a past random sample of lifetimes, the pivotal quantity averaging (PQA) approach is suggested. The PQA approach represents a simple and computationally attractive statistical technique. In this case, the transition from the original problem to the equivalent transformed problem (in terms of pivotal quantities and ancillary factors) is carried out via invariant embedding a sample statistic in the original problem. The approach allows one to eliminate unknown parameters from the problem and to find the better decision rules, which have smaller risk than any of the well-known decision rules. Unlike the Bayesian approach, the proposed approach is independent of the choice of priors. For illustration, numerical examples are given.
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Ye, Wei-Cheng, and Jia-Ching Wang. "Multilabel Classification Based on Graph Neural Networks." In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99681.

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Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise.
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Conference papers on the topic "Out-of-sample Embedding"

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Li, Chun-guang, Jun Guo, and Xiangfei Nie. "Learning Geodesic Metric for Out-of-Sample Extension of Isometric Embedding." In 2006 International Conference on Computational Intelligence and Security. IEEE, 2006. http://dx.doi.org/10.1109/iccias.2006.294174.

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Dornaika, F., and B. Raduncanu. "Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition." In 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2013. http://dx.doi.org/10.1109/cvprw.2013.127.

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Guo, Yi, Junbin Gao, and Paul W. Kwan. "Learning Out-Of Sample Mapping in Non-Vectorial Data Reduction using Constrained Twin Kernel Embedding." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370108.

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Yan, Xinyu, Lijun Zhang, and Wu-Jun Li. "Semi-Supervised Deep Hashing with a Bipartite Graph." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/452.

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Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional methods with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (DHBG), to simultaneously learn embeddings, features and hash codes. More specifically, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, DHBG is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of DHBG to support out-of-sample extensions. Experimental results on real datasets show that our DHBG outperforms state-of-the-art hashing methods.
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Pinto, Preston A., Stephen A. Sarles, Donald J. Leo, Michael Philen, Hunter A. Champion, Sarah B. Black, and Harry C. Dorn. "Bio-Inspired Flow Sensors Fabricated From Carbon Nanomaterials." In ASME 2011 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/smasis2011-5168.

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Carbon-based flow sensors can be made by embedding carbon nanotubes (CNT) into a polymeric substrate. Specifically, when a conductive aqueous solution flows over the surface of the exposed CNT, a flow-dependent voltage is generated. The carbonaceous flow sensors fabricated in our work were all tested in salt water (5% NaCl). In order to measure the surface coverage of the CNT coated sensors, the electrical resistance across the surface of each sample was measured. Electrical Impedance Spectroscopy (EIS) measurements were also carried out in order to understand the electrical relationship between the sensor and the salt water. In order to study the surface topology and morphology of the flow sensors, scanning electron microscopy (SEM) was used. Voltage measurements of sensors with different levels of resistance were tested in varying fluid velocities. The least resistive sensor showed small, but detectable changes in voltages, while higher resistance sensors showed less response. On the other hand, the average current did not change with varying flow conditions for any of the sensors.
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Ye, Yanfang, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, and Fudong Shao. "Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/576.

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The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i.e., app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. To efficiently classify nodes (e.g., apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.
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Yañez Gonzalez, A., C. C. Pilgrim, J. P. Feist, P. Y. Sollazzo, F. Beyrau, and A. L. Heyes. "On-Line Temperature Measurement Inside a Thermal Barrier Sensor Coating During Engine Operation." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25936.

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Existing thermal barrier coatings (TBC) can be adapted enhancing their functionalities such that they not only protect critical components from hot gases, but also can sense their own material temperature or other physical properties. The self-sensing capability is introduced by embedding optically active rare earth ions into the thermal barrier ceramic. When illuminated by light the material starts to phosphoresce and the phosphorescence can provide in-situ information on temperature, phase changes, corrosion or erosion of the coating subject to the coating design. The integration of an on-line temperature detection system enables the full potential of TBCs to be realised due to improved accuracy in temperature measurement and early warning of degradation. This in turn will increase fuel efficiency and will reduce CO2 emissions. This paper reviews the previous implementation of such a measurement system into a Rolls-Royce jet engine using dysprosium doped yttrium-stabilised-zirconia as a single layer and a dual layer sensor coating material. The temperature measurements were carried out on cooled and uncooled components on a combustion chamber liner and on nozzle guide vanes respectively. The paper investigates the interpretation of those results looking at coating thickness effects and temperature gradients across the TBC. For the study a specialised cyclic thermal gradient burner test rig was operated and instrumented using equivalent instrumentation to that used for the engine test. This unique rig enables the controlled heating of the coatings at different temperature regimes. A long-wavelength pyrometer was employed detecting the surface temperature of the coating in combination with the phosphorescence detector. A correction was applied to compensate for changes in emissivity using two methods. A thermocouple was used continuously measuring the substrate temperature of the sample. Typical gradients across the coating are less than 1K/μm. As the excitation laser penetrates the coating it generates phosphorescence from several locations throughout the coating and hence provides an integrated signal. The study successfully proved that the temperature indication from the phosphorescence coating remains between the surface and substrate temperature for all operating conditions. This demonstrates the possibility to measure inside the coating closer to the bond coat. The knowledge of the bond coat temperature is relevant to the growth of the thermally grown oxide which is linked to the delamination of the coating and hence determines its life. Further, the data is related to a one dimensional phosphorescence model determining the penetration depth of the laser and the emission. Note: a video of the measurement system can be watched under: [http://www.youtube.com/watch?v=T6uXN1__Z7I].
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