Multilinear Subspace Learning
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Multilinear Subspace Learning
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Author : Haiping Lu
language : en
Publisher: CRC Press
Release Date : 2013-12-11
Multilinear Subspace Learning written by Haiping Lu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-11 with Computers categories.
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today’s most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications. The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB® source code, data, and other materials are available at www.comp.hkbu.edu.hk/~haiping/MSL.html
Multilinear Subspace Learning
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Author : Haiping Lu
language : en
Publisher: CRC Press
Release Date : 2013-12-11
Multilinear Subspace Learning written by Haiping Lu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-11 with Computers categories.
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniqu
Multilinear Subspace Learning Via Linear Tansforms And Grassmannian Manifold Analysis
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Author : Cagri Ozdemir
language : en
Publisher:
Release Date : 2023
Multilinear Subspace Learning Via Linear Tansforms And Grassmannian Manifold Analysis written by Cagri Ozdemir and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Furthermore, in general, the number of observations is relatively small compared to the feature vector dimension potentially resulting in poor conditioning (referred to as the small sample size problem). Due to these issues, particularly when dealing with higher-order data with high dimensionality, there has been a growing interest in multilinear subspace learning (MSL) to maintain the natural representation of multidimensional arrays (commonly referred to as tensors). To best explore, analyze, and provide insights from such data, new mathematical tools are required in an effort to bridge the gap between traditional machine learning models and their multilinear counterparts. In this dissertation, we present new approaches and formulate mathematical theories to deal with such data using a multilinear (tensor-tensor) perspective. In particular, we provide insights into several different application areas within the machine learning community and illustrate how multilinear extensions can be achieved.
Multilinear Subspace Learning For Face And Gait Recognition
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Author : Haiping Lu
language : en
Publisher:
Release Date : 2008
Multilinear Subspace Learning For Face And Gait Recognition written by Haiping Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects.This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance.Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.
Principles Of Machine Learning
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Author : Wenmin Wang
language : en
Publisher: Springer Nature
Release Date : 2024-10-26
Principles Of Machine Learning written by Wenmin Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Mathematics categories.
Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples. The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled “Perspectives,” comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, “Frameworks”: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, “Paradigms,” encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, “Tasks”: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction. This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Advanced Signal Processing
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Author : Stergios Stergiopoulos
language : en
Publisher: CRC Press
Release Date : 2017-09-29
Advanced Signal Processing written by Stergios Stergiopoulos and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-29 with Technology & Engineering categories.
Discover the Applicability, Benefits, and Potential of New Technologies As advances in algorithms and computer technology have bolstered the digital signal processing capabilities of real-time sonar, radar, and non-invasive medical diagnostics systems, cutting-edge military and defense research has established conceptual similarities in these areas. Now civilian enterprises can use government innovations to facilitate optimal functionality of complex real-time systems. Advanced Signal Processing details a cost-efficient generic processing structure that exploits these commonalities to benefit commercial applications. Learn from a Renowned Defense Scientist, Researcher, and Innovator The author preserves the mathematical focus and key information from the first edition that provided invaluable coverage of topics including adaptive systems, advanced beamformers, and volume visualization methods in medicine. Integrating the best features of non-linear and conventional algorithms and explaining their application in PC-based architectures, this text contains new data on: Advances in biometrics, image segmentation, registration, and fusion techniques for 3D/4D ultrasound, CT, and MRI Fully digital 3D/ (4D: 3D+time) ultrasound system technology, computing architecture requirements, and relevant implementation issues State-of-the-art non-invasive medical procedures, non-destructive 3D tomography imaging and biometrics, and monitoring of vital signs Cardiac motion correction in multi-slice X-ray CT imaging Space-time adaptive processing and detection of targets interference-intense backgrounds comprised of clutter and jamming With its detailed explanation of adaptive, synthetic-aperture, and fusion-processing schemes with near-instantaneous convergence in 2-D and 3-D sensors (including planar, circular, cylindrical, and spherical arrays), the quality and illustration of this text’s concepts and techniques will make it a favored reference.
Handbook Of Robust Low Rank And Sparse Matrix Decomposition
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Author : Thierry Bouwmans
language : en
Publisher: CRC Press
Release Date : 2016-09-20
Handbook Of Robust Low Rank And Sparse Matrix Decomposition written by Thierry Bouwmans and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-20 with Computers categories.
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.
Structural Syntactic And Statistical Pattern Recognition
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Author : Georgy Gimel ́farb
language : en
Publisher: Springer
Release Date : 2012-10-22
Structural Syntactic And Statistical Pattern Recognition written by Georgy Gimel ́farb and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-22 with Computers categories.
This volume constitutes the refereed proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2012) and Statistical Techniques in Pattern Recognition (SPR 2012), held in Hiroshima, Japan, in November 2012 as a satellite event of the 21st International Conference on Pattern Recognition, ICPR 2012. The 80 revised full papers presented together with 1 invited paper and the Pierre Devijver award lecture were carefully reviewed and selected from more than 120 initial submissions. The papers are organized in topical sections on structural, syntactical, and statistical pattern recognition, graph and tree methods, randomized methods and image analysis, kernel methods in structural and syntactical pattern recognition, applications of structural and syntactical pattern recognition, clustering, learning, kernel methods in statistical pattern recognition, kernel methods in statistical pattern recognition, as well as applications of structural, syntactical, and statistical methods.
Feature Learning And Understanding
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Author : Haitao Zhao
language : en
Publisher: Springer Nature
Release Date : 2020-04-03
Feature Learning And Understanding written by Haitao Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-03 with Science categories.
This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.
Machine Learning And Knowledge Discovery In Databases
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Author : Hendrik Blockeel
language : en
Publisher: Springer
Release Date : 2013-08-28
Machine Learning And Knowledge Discovery In Databases written by Hendrik Blockeel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-28 with Computers categories.
This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.