High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications - Hardcover
Synopsis
Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
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About the Authors
John Wright is an Associate Professor in the Electrical Engineering Department and the Data Science Institute at Columbia University.
Yi Ma is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a Fellow of the IEEE, ACM, and SIAM.
"About this title" may belong to another edition of this title.
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High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
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High-dimensional Data Analysis With Low-dimensional Models : Principles, Computation, and Applications
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High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
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High-dimensional Data Analysis With Low-dimensional Models: Principles, Computation, and Applications
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High-dimensional Data Analysis With Low-dimensional Models : Principles, Computation, and Applications
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High-dimensional Data Analysis With Low-dimensional Models : Principles, Computation, and Applications
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High-dimensional Data Analysis With Low-dimensional Models : Principles, Computation, and Applications
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High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in co. Seller Inventory # 487330421
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High-Dimensional Data Analysis with Low-Dimensional Models (Hardcover)
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ISBN 10: 1108489737
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Hardcover. Condition: new. Hardcover. Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candes. A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in computer science, data science, and electrical engineering. With foreword by Emmanuel Candes. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781108489737
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