A feature analysis for dimension reduction based on a data generation model with class factors and environment factors

Currently, high-dimensional data such as image data is widely used in the domain of pattern classification and signal processing. When using high-dimensional data, feature analysis methods such as PCA (principal component analysis) and LDA (linear discriminant analysis) are usually required in order to reduce memory usage or computational complexity as well as to increase classification performance. We propose a feature analysis method for dimension reduction based on a data generation model that is composed of two types of factors: class factors and environment factors. The class factors, which are prototypes of the classes, contain important information required for discriminating between various classes. The environment factors, which represent distortions of the class prototypes, need to be diminished for obtaining high class separability. Using the data generation model, we aimed to exclude environment factors and extract low-dimensional class factors from the original data. By performing computational experiments on artificial data sets and real facial data sets, we confirmed that the proposed method can efficiently extract low-dimensional features required for classification and has a better performance than the conventional methods. Crown