Discrete Principal Component Analysis
نویسندگان
چکیده
This article presents a unified theory for analysis of components in discrete data, and compares the methods with techniques such as independent component analysis (ICA), non-negative matrix factorisation (NMF) and latent Dirichlet allocation (LDA). The main families of algorithms discussed are mean field, Gibbs sampling, and Rao-Blackwellised Gibbs sampling. Applications are presented for voting records from the United States Senate for 2003, and the use of components in subsequent classification.
منابع مشابه
Principal component analysis or factor analysis different wording or methodological fault?
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