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oaioai:cds.cern.ch:2761916

Generalized low rank models

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Publication date
1 January 2016
Publisher
'Now Publishers'

Abstract

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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Last time updated on 29/04/2021

This paper was published in CERN Document Server.

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