| #5322486 in Books | 2016-05-03 | Original language:English | 9.21 x.30 x6.14l,.46 | File type: PDF | 142 pages||About the Author|Stephen Boyd received his PhD from the University of California, Berkeley. Since 1985 he has been a member of the Electrical Engineering Department at Stanford University, where he is now Professor and Director of the Information Systems Laborat
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well-known techniques in data analysis, such as nonnegative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. T...
You can specify the type of files you want, for your gadget.Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning) | Madeleine Udell, Corinne Horn, Reza Zadeh. I really enjoyed this book and have already told so many people about it!
