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Evolving principal components analysis

Qualitative exploratory analysis for example principal component analysis (PCA), fixed size image window-evolving factor analysis (FSIW-EFA). [Pg.361]

More commonly, we are faced with the need for mathematical resolution of components, using their different patterns (or spectra) in the various dimensions. That is, literally, mathematical analysis must supplement the chemical or physical analysis. In this case, we very often initially lack sufficient model information for a rigorous analysis, and a number of methods have evolved to "explore the data", such as principal components and "self-modeling analysis (21), cross correlation (22). Fourier and discrete (Hadamard,. . . ) transforms (23) digital filtering (24), rank annihilation (25), factor analysis (26), and data matrix ratioing (27). [Pg.68]

See other pages where Evolving principal components analysis is mentioned: [Pg.274]    [Pg.69]    [Pg.274]    [Pg.69]    [Pg.62]    [Pg.280]    [Pg.31]    [Pg.305]    [Pg.76]    [Pg.511]    [Pg.217]    [Pg.12]    [Pg.35]    [Pg.453]    [Pg.156]    [Pg.38]    [Pg.362]    [Pg.399]    [Pg.423]    [Pg.3009]    [Pg.293]    [Pg.258]    [Pg.541]    [Pg.163]    [Pg.311]    [Pg.64]   
See also in sourсe #XX -- [ Pg.274 ]



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