Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data

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12

초록

We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets.

키워드

Dependence mapsDimensionality reductionDependenceMarkov chainREGULARIZATIONEIGENMAPS
제목
Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data
저자
Lee, KichunGray, AlexanderKim, Heeyoung
DOI
10.1007/s10618-012-0267-9
발행일
2013-05
유형
Article
저널명
Data Mining and Knowledge Discovery
26
3
페이지
512 ~ 532