Seghouane, Abd-Krim; Cichocki, Andrzej
Recently, the technique of principal component analysis (PCA) has been expressed as the maximum likelihood solution for a generative latent variable model. A central issue in PCA is choosing the number of principal components to retain. This can be considered as a problem of model selection. In this paper, the probabilistic reformulation of PCA is used as a basis for a Bayasian approach of PCA to derive a model selection criterion for determining the true dimensionality of data. The proposed...[Show more]
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