Generalized Low Rank Models
نویسندگان
چکیده
منابع مشابه
Generalized Low Rank Models
Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix 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...
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Learning of low-rank matrices is fundamental to many machine learning applications. A state-ofthe-art algorithm is the rank-one matrix pursuit (R1MP). However, it can only be used in matrix completion problems with the square loss. In this paper, we develop a more flexible greedy algorithm for generalized low-rank models whose optimization objective can be smooth or nonsmooth, general convex or...
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ژورنال
عنوان ژورنال: Foundations and Trends® in Machine Learning
سال: 2016
ISSN: 1935-8237,1935-8245
DOI: 10.1561/2200000055