[PDF.17ka] Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning)

Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning)

[PDF.qn95] Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning)

Generalized Low Rank Models  Madeleine Udell, Corinne Horn, Reza Zadeh epub
Generalized Low Rank Models  Madeleine Udell, Corinne Horn, Reza Zadeh pdf download
Generalized Low Rank Models  Madeleine Udell, Corinne Horn, Reza Zadeh pdf file
Generalized Low Rank Models  Madeleine Udell, Corinne Horn, Reza Zadeh audiobook
Generalized Low Rank Models  Madeleine Udell, Corinne Horn, Reza Zadeh book review
Generalized Low Rank Models  Madeleine Udell, Corinne Horn, Reza Zadeh summary

 | #6628642 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 easily download any file type for your gadget.Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning)   |  Madeleine Udell, Corinne Horn, Reza Zadeh. I was recommended this book by a dear friend of mine.

Download Now