{"product_id":"generalized-principal-component-analysis-paperback","title":"Generalized Principal Component Analysis - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eRené Vidal\u003c\/b\u003e (Author), \u003cb\u003eYi Ma\u003c\/b\u003e (Author), \u003cb\u003eShankar Sastry\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRené\u003c\/b\u003e\u003cb\u003e Vidal\u003c\/b\u003e is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. \u003c\/p\u003eYi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. \u003cb\u003eS. Shankar Sastry\u003c\/b\u003e is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRené\u003c\/b\u003e\u003cb\u003e Vidal\u003c\/b\u003e is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. \u003c\/p\u003eYi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. \u003cb\u003eS. Shankar Sastry\u003c\/b\u003e is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRen?\u003c\/b\u003e\u003cb\u003e Vidal\u003c\/b\u003e is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eYi Ma\u003c\/b\u003e is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eS. Shankar Sastry\u003c\/b\u003e is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 566\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.21 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e April 14, 2018\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43154155044927,"sku":"9781493979127","price":165.22,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/Tjds3BSliu9781493979127.webp?v=1776954242","url":"https:\/\/dhlswag.com\/products\/generalized-principal-component-analysis-paperback","provider":"BBB","version":"1.0","type":"link"}