Nonlinear Principal Component Analysis and Its Applications - download pdf or read online

By Yuichi Mori, Masahiro Kuroda, Naomichi Makino

ISBN-10: 981100157X

ISBN-13: 9789811001574

ISBN-10: 9811001596

ISBN-13: 9789811001598

This ebook expounds the main and comparable functions of nonlinear critical part research (PCA), that is invaluable way to research combined size degrees information. within the half facing the primary, after a quick advent of standard PCA, a PCA for specific information (nominal and ordinal) is brought as nonlinear PCA, within which an optimum scaling strategy is used to quantify the explicit variables. The alternating least squares (ALS) is the most set of rules within the process. a number of correspondence research (MCA), a different case of nonlinear PCA, can also be brought. All formulations in those equipment are built-in within the similar demeanour as matrix operations. simply because any dimension degrees information will be handled constantly as numerical facts and ALS is the most important device for estimations, the equipment can be used in quite a few fields reminiscent of biometrics, econometrics, psychometrics, and sociology. within the functions a part of the ebook, 4 functions are brought: variable choice for combined size degrees facts, sparse MCA, joint size aid and clustering tools for specific information, and acceleration of ALS computation. The variable choice equipment in PCA that initially have been built for numerical info will be utilized to any different types of size degrees by utilizing nonlinear PCA. Sparseness and joint size aid and clustering for nonlinear facts, the result of contemporary stories, are extensions acquired through a similar matrix operations in nonlinear PCA. ultimately, an acceleration set of rules is proposed to lessen the matter of computational rate within the ALS generation in nonlinear multivariate tools. This booklet therefore offers the usefulness of nonlinear PCA which are utilized to varied dimension degrees information in different fields. in addition, it covers the most recent issues together with the extension of the conventional statistical technique, newly proposed nonlinear tools, and computational potency within the methods.

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Extra resources for Nonlinear Principal Component Analysis and Its Applications

Sample text

001 until the number of variables is nine. 2 4 Var 18 -6 -4 -2 0 2 4 6 1st Fig. 4 -4 Var 11 0 2 1st Fig. 2 Selection results (Artificial data, Forward–backward selection, r = 2, RV -coefficient) 14 14 14 15 16 17 |2 2 6 10 10 10 12 12 12 13 14 14 15 15 15 16 17 |2 8 6 10 12 12 12 14 14 14 14 15 15 16 16 16 17 |2 8 11 12 12 14 14 14 15 15 15 15 16 16 17 17 17 |2 14 14 14 14 14 16 16 16 16 16 16 16 17 17 18 18 | 18 18 18 18 18 18 18 18 18 18 17 17 17 18 18 18 19 | 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 RV -coef.

P) are alternately optimized; the pre-multiplied rotation matrix S is optimized given the Ti (i = 1, . . , p), and the post-multiplied matrices Ti (i = 1, . . , p) is optimized given the matrix S. It should be noted that the rotational freedom of the quantification matrices in nonlinear PCA disapears because all dimensions of the quantifications are set to be one. 3 Sparse MCA In sparse MCA, there are mainly two approaches for obtaining the sparse solutions. In one approach, the MCA objective function is combined with a penalty function that penalizes the component weight matrix (Bernard et al.

However, as mentioned in Sect. 1, the loading matrix can be defined in the MCA formulation proposed by Murakami et al. (1999) in the same manner as PCA. 2) i=1 where Gi (n × Ki ) is an indicator matrix, Qi (Ki × Ri ) is a quantification matrix, and Ti (Ri × Ri ) is an orthonormal matrix (i = 1, . . , p). Murakami (1999) modified an orthomax criterion in three-way PCA for MCA solutions in which the loading matrix is rotated towards the simple structure by pre- and post-multiplied rotation matrices S and Ti (i = 1, .

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Nonlinear Principal Component Analysis and Its Applications by Yuichi Mori, Masahiro Kuroda, Naomichi Makino

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