A Generalized Linear Model for Principal Component Analysis of Binary Data.
作者： Andrew I. Schein , Lawrence K. Saul , Lyle H. Ungar
DOI:
关键词: Generalized linear model 、 Generalized linear mixed model 、 General linear model 、 Principal component analysis 、 Logistic regression 、 Proper linear model 、 Artificial intelligence 、 Pattern recognition 、 Linear predictor function 、 Algorithm 、 Principal component regression 、 Mathematics
摘要: We investigate a generalized linear model for dimensionality reduction of binary data. The is related to principal component analysis (PCA) in the same way that logistic regression regression. Thus we refer as PCA. In this paper, derive an alternating least squares method estimate basis vectors and coefficients PCA model. resulting updates have simple closed form are guaranteed at each iteration improve model’s likelihood. evaluate performance PCA—as measured by reconstruction error rates—on data sets drawn from four real world applications. general, find much better suited modeling than conventional
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