Abstract
Principal component analysis (PCA) is a statistical technique that is employed to reduce the dimensionality of a data set in which there are a large number of interrelated variables. This reduction is obtained by transforming to a new set of variables, the principal components, which are uncorrelated and which are ordered so that the first few retain most of the variation present in all of the original variables. In other words, PCA searches for a set of statistically de-correlated features as an efficient representation of data, while retaining most of the intrinsic information in the data. PCA is an old and well-known technique in multivariate analysis. It was independently developed as a technique to analyze the correlation structure of data. Applications of PCA include data compression, noise reduction, feature extraction, and data analysis. This chapter describes PCA; how it is determined; PCA neural networks; biological background of PCA neural networks; minor component analysis; and nonlinear PCA.
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