LEADER 05374nam 22008415 450 001 9910254071003321 005 20200702180943.0 010 $a0-387-87811-4 024 7 $a10.1007/978-0-387-87811-9 035 $a(CKB)3710000000653196 035 $a(SSID)ssj0001665965 035 $a(PQKBManifestationID)16455443 035 $a(PQKBTitleCode)TC0001665965 035 $a(PQKBWorkID)15000762 035 $a(PQKB)11235445 035 $a(DE-He213)978-0-387-87811-9 035 $a(MiAaPQ)EBC6313098 035 $a(MiAaPQ)EBC5584556 035 $a(Au-PeEL)EBL5584556 035 $a(OCoLC)946944433 035 $a(PPN)193442361 035 $a(EXLCZ)993710000000653196 100 $a20160411d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aGeneralized Principal Component Analysis /$fby René Vidal, Yi Ma, Shankar Sastry 205 $a1st ed. 2016. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2016. 215 $a1 online resource (XXXII, 566 p. 121 illus., 83 illus. in color.) 225 1 $aInterdisciplinary Applied Mathematics,$x0939-6047 ;$v40 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-387-87810-6 327 $aPreface -- Acknowledgments -- Glossary of Notation -- Introduction -- I Modeling Data with Single Subspace -- Principal Component Analysis -- Robust Principal Component Analysis -- Nonlinear and Nonparametric Extensions -- II Modeling Data with Multiple Subspaces -- Algebraic-Geometric Methods -- Statistical Methods -- Spectral Methods -- Sparse and Low-Rank Methods -- III Applications -- Image Representation -- Image Segmentation -- Motion Segmentation -- Hybrid System Identification -- Final Words -- Appendices -- References -- Index. 330 $aThis book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley. 410 0$aInterdisciplinary Applied Mathematics,$x0939-6047 ;$v40 606 $aSystem theory 606 $aOptical data processing 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aStatistics  606 $aAlgebraic geometry 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aAlgebraic Geometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M11019 615 0$aSystem theory. 615 0$aOptical data processing. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aStatistics . 615 0$aAlgebraic geometry. 615 14$aSystems Theory, Control. 615 24$aImage Processing and Computer Vision. 615 24$aSignal, Image and Speech Processing. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aAlgebraic Geometry. 676 $a519.5354 700 $aVidal$b René$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755927 702 $aMa$b Yi$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSastry$b Shankar$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254071003321 996 $aGeneralized Principal Component Analysis$92283989 997 $aUNINA