Nonlinear principle component analysis using local probability

Principle component analysis (PCA) is a dimensionality reduction technique for data analysis and processing, and it is a linear method that can maximize the variance of data. In this paper, to achieve the high efficiency for data classification, nonlinear PCA using local probability is proposed. Parameters are extracted from each distribution of data and mapping function of data set is made using the relation of the extracted parameters. Nonlinear PCA is performed in new projection feature space. The experimental result is conducted to verify its efficiency compared with the classical linear PCA.