Bayesian Matrix Factorization for Face Recognition
Jiang Bian, Xinbo Gao, Xiumei Wang · 2013
Principal Component Analysis (PCA), one of the most popular dimensionality reduction algorithms, has three particular problems: the number of eigenvalues is limited by the two direction dimensions; it assumes for reconstruction of Gaussian distributed data, not for classification problems; it assumes that eigenvalues and eigenvectors is all linear. In this paper, we proposed a Bayesian Mixture Model, Bayesian Mixture of Inverse Regression (BMI), to deal with these three problems as preprocessing method and then use classic algorithms, Discriminative Locality Alignment (DLA) and Fishers Linear Discriminant Analysis (FLDA), to classify the test data into different topics. Through empirical studies on the face recognition demonstrate the effectiveness of DLA & BMI and LDA & BMI are more effective than DLA & PCA and LDA & PCA.