基于概率PCA模型的字符图像子空间维数确定方法

central issue in PCA is choosing the number of principal components to retain. However, most studies assume a known dimension or determine it heuristically, though there exist a number of model selection criteria in the literature of statistics. In this paper, the probabilistic reformulation of PCA is used and a model selection criterion for determining the true dimensionality of data including Akaike’s information criterion (AIC), the consistent Akaika’s information criterion (CAIC), and the Bayesian inference criterion (BIC) is derived. At last, aimed to the character images, these parameters could affect the determination of the subspace dimension is analyzed in detail.