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MOST INFORMATIVE COMPONENT ANALYSIS

Yaping Jing, Hong Lei and Yingcun Xia
Statistica Sinica
Vol. 24, No. 3 (July 2014), pp. 1195-1214
Stable URL: http://www.jstor.org/stable/24310984
Page Count: 20
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MOST INFORMATIVE COMPONENT ANALYSIS
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Abstract

We extend the principal component analysis (PCA) to the investigation of nonlinear dependence among variables, called most informative component analysis (MICA). The most informative components are linear combinations of the variables that capture both linear and nonlinear dependence among the variables. Compared with the existing extensions such as the principal curve and the kernel PCA, MICA is more interpretable and thus more meaningful in statistical analysis. Properties of MICA are investigated, the estimation method is developed, and asymptotics of the estimators are obtained. Data sets are analyzed to illustrate the usefulness of MICA.

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