Multilinear Subspace Learning Via Linear Tansforms and Grassmannian Manifold Analysis PDF Download
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Author: Cagri Ozdemir
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Cagri Ozdemir
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Haiping Lu
Publisher: CRC Press
ISBN: 1439857245
Category : Computers
Languages : en
Pages : 298
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Book Description
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
Author: Konstantinos N. Plataniotis
Publisher: Springer Science & Business Media
ISBN: 3662041863
Category : Computers
Languages : en
Pages : 368
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Book Description
Reporting the state of the art of colour image processing, this monograph fills a gap in the literature on digital signal and image processing. It contains numerous examples and pictures of colour image processing results, plus a library of algorithms implemented in C.
Author: P.-A. Absil
Publisher: Princeton University Press
ISBN: 1400830249
Category : Mathematics
Languages : en
Pages : 240
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Book Description
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists.
Author: René Vidal
Publisher: Springer
ISBN: 0387878114
Category : Science
Languages : en
Pages : 590
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Book Description
This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Author: Hongyu Guo
Publisher: World Scientific
ISBN: 9811241031
Category : Mathematics
Languages : en
Pages : 246
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Book Description
Tensors have numerous applications in physics and engineering. There is often a fuzzy haze surrounding the concept of tensor that puzzles many students. The old-fashioned definition is difficult to understand because it is not rigorous; the modern definitions are difficult to understand because they are rigorous but at a cost of being more abstract and less intuitive.The goal of this book is to elucidate the concepts in an intuitive way but without loss of rigor, to help students gain deeper understanding. As a result, they will not need to recite those definitions in a parrot-like manner any more. This volume answers common questions and corrects many misconceptions about tensors. A large number of illuminating illustrations helps the reader to understand the concepts more easily.This unique reference text will benefit researchers, professionals, academics, graduate students and undergraduate students.
Author: Richard Hartley
Publisher: Cambridge University Press
ISBN: 1139449141
Category : Computers
Languages : en
Pages : 676
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Book Description
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
Author: David Eisenbud
Publisher: Cambridge University Press
ISBN: 1107017084
Category : Mathematics
Languages : en
Pages : 633
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Book Description
3264, the mathematical solution to a question concerning geometric figures.
Author: Loring W. Tu
Publisher: Springer Science & Business Media
ISBN: 1441974008
Category : Mathematics
Languages : en
Pages : 426
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Book Description
Manifolds, the higher-dimensional analogs of smooth curves and surfaces, are fundamental objects in modern mathematics. Combining aspects of algebra, topology, and analysis, manifolds have also been applied to classical mechanics, general relativity, and quantum field theory. In this streamlined introduction to the subject, the theory of manifolds is presented with the aim of helping the reader achieve a rapid mastery of the essential topics. By the end of the book the reader should be able to compute, at least for simple spaces, one of the most basic topological invariants of a manifold, its de Rham cohomology. Along the way, the reader acquires the knowledge and skills necessary for further study of geometry and topology. The requisite point-set topology is included in an appendix of twenty pages; other appendices review facts from real analysis and linear algebra. Hints and solutions are provided to many of the exercises and problems. This work may be used as the text for a one-semester graduate or advanced undergraduate course, as well as by students engaged in self-study. Requiring only minimal undergraduate prerequisites, 'Introduction to Manifolds' is also an excellent foundation for Springer's GTM 82, 'Differential Forms in Algebraic Topology'.
Author: Gregory S. Chirikjian
Publisher: Springer Science & Business Media
ISBN: 0817648038
Category : Mathematics
Languages : en
Pages : 397
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Book Description
This unique two-volume set presents the subjects of stochastic processes, information theory, and Lie groups in a unified setting, thereby building bridges between fields that are rarely studied by the same people. Unlike the many excellent formal treatments available for each of these subjects individually, the emphasis in both of these volumes is on the use of stochastic, geometric, and group-theoretic concepts in the modeling of physical phenomena. Stochastic Models, Information Theory, and Lie Groups will be of interest to advanced undergraduate and graduate students, researchers, and practitioners working in applied mathematics, the physical sciences, and engineering. Extensive exercises and motivating examples make the work suitable as a textbook for use in courses that emphasize applied stochastic processes or differential geometry.
