Generalized low rank models

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Generalized Low Rank Models extends the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.

Detalles Bibliográficos
Autores principales: Udell, Madeleine, Horn, Corinne, Zadeh, Reza, Boyd, Stephen
Lenguaje:eng
Publicado: Now Publishers 2016
Materias:
XX
Acceso en línea:http://cds.cern.ch/record/2761916
Descripción
Sumario:Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Generalized Low Rank Models extends the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.

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