By René Vidal,Yi Ma,S.S. Sastry
This publication presents a finished creation to the newest advances within the mathematical conception and computational instruments for modeling high-dimensional facts drawn from one or a number of low-dimensional subspaces (or manifolds) and in all probability corrupted by means of noise, gross error, or outliers. This difficult job calls for the advance of latest algebraic, geometric, statistical, and computational equipment for effective and powerful estimation and segmentation of 1 or a number of subspaces. The publication additionally offers attention-grabbing real-world purposes of those new equipment in photograph processing, snapshot and video segmentation, face attractiveness and clustering, and hybrid process id and so on.
This publication is meant to function a textbook for graduate scholars and starting researchers in information technology, desktop studying, computing device imaginative and prescient, picture and sign processing, and platforms concept. It comprises plentiful illustrations, examples, and routines and is made principally self-contained with 3 Appendices which survey easy strategies and rules from facts, optimization, and algebraic-geometry utilized in this book.
René Vidal is a Professor of Biomedical Engineering and Director of the imaginative and prescient Dynamics and studying Lab on the Johns Hopkins University.
Yi Ma is govt Dean and Professor on the institution of knowledge technology and know-how at ShanghaiTech University. S. Shankar Sastry is Dean of the school of Engineering, Professor of electric Engineering and desktop technological know-how and Professor of Bioengineering on the college of California, Berkeley.
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Generalized Principal Component Analysis (Interdisciplinary Applied Mathematics) by René Vidal,Yi Ma,S.S. Sastry