Principal Axis Analysis
نویسندگان
چکیده
Principal axis analysis rotates principal components to optimally detect cluster structure, rotation being based on a second spectral decomposition identifying preferred axes in the sphered data. As such, it complements principal component analysis as an extremely fast, general, projection pursuit method, particularly well-suited to detecting mixtures of elliptical distributions. Examples show that it can perform comparably to linear discriminant analysis without using group (cluster) membership information, while its sphered and unsphered forms offer complementary views. Points of contact with a range of multivariate methods are noted and further developments briefly indicated.
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تاریخ انتشار 2006