《Generalized Principal Component Analysis》, a 3-year joint work by Prof. Rene Vidal, Yi Ma, and S. Shankar Sastry, has been published by Springer-Verlag.
《Generalized Principal Component Analysis》(GPCA) focuses on Unsupervised Learning problems, while the prevalent DNN solves the Supervised Learning problems. GPCA not only links PCA, a hundred years old method, with the current Compressive Sensing, but also covers Algebraic Geometry, Statistics, High Dimensional Data Processing, and Optimization Algorithms etc; with respect to applications, it involves all fields of science and engineering.
This book provides detailed derivations and concise conclusions, and is indispensable for starting researchers in Data Science. Moreover, GPCA is not merely theory composition, but a teaching justification of the three authors: during the composition, Prof. Yi Ma and other two professors have used the content of this book for graduate courses at ShanghaiTech University, UIUC, UC Berkeley and Johns Hopkins University, and achieved excellent teaching performance.
《Generalized Principal Component Analysis》can be bought from Amazon.
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