Nonlinear Principal Component Analysis and Related Techniques
نویسنده
چکیده
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for many different purposes and in many different contexts. PCA is the basis for low rank least squares approximation of a data matrix, for finding linear combinations with maximum or minimum variance, for fitting bilinear biplot models, for computing factor analysis approximations, and for studying regression with errors in variables. It is closely related to simple correspondence analysis (CA) and multiple correspondence analysis (MCA), which are discussed in Chapters XX and YY of this book.
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