[PDF.15jl] Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning)

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

[PDF.fa32] Generalized Low Rank Models (Foundations and Trends(r) in Machine Learning)

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 | #7051872 in Books |  2016-05-03 | Original language:English |  9.21 x.30 x6.14l,.46 | File type: PDF | 142 pages

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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...

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