Generalized Principal Component Analysis

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.

• Preface.- 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.

"The book under review provides a timely and comprehensive description of the classic and modern PCA-based and other dimension reduction techniques. Although the topic of dimension reduction has been briefly converted in quite a few books and review papers, this book should be especially applauded for its unique depth and comprehensiveness. ... Overall, this is one of the best books on PCA and modern dimension reduction techniques and should expect an increasing popularity." (Steven (Shuangge) Ma, Mathematical Reviews, January, 2017)