SEPARABLE PCA FOR IMAGE CLASSIFICATION

As an alternative to standard PCA, matrix-based image dimensionality reduction methods have recently been proposed and have gained attention due to reported computational efficiency and robust performance in classification. We unify all of these methods through one concept: Separable Principle Component Analysis (SPCA). We show that the proposed matrix methods are either equivalent to, special cases of, or approximations to SPCA. We include performance comparisons of the methods on two face data sets and a handwritten digit data set. The empirical results indicate that two existing methods, BD-PCA and its variant NGLRAM, are very good, efficiently computable, approximate solutions to practical SPCA problems.