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 criterion is similar to the Bayesian Information Criterion, BIC, with a particular goodness of fit term and it is consistent. A simulation example that illustrates its performance for the determination of the number of principal components to be retained is presented.
|Journal||European Signal Processing Conference|
|Publication status||Published - 2006|
|Event||14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy|
Duration: 4 Sep 2006 → 8 Sep 2006