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Measuring Statistical Dependence and its Applications in Machine Learning

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자료유형E-Book
개인저자Jin, Ze.
단체저자명Cornell University. Statistics.
서명/저자사항Measuring Statistical Dependence and its Applications in Machine Learning.
발행사항[S.l.] : Cornell University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항94 p.
소장본 주기School code: 0058.
ISBN9780438345256
일반주기 Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: David S. Matteson.
요약My PhD research focuses on measuring and testing mutual dependence and conditional mean dependence, and applying it to Machine Learning problems, which is elaborated in the following four chapters:
요약Chapter 1 -- We propose three new measures of mutual dependence between multiple random vectors. Each measure is zero if and only if the random vectors are mutually independent. The first generalizes distance covariance from pairwise dependence
요약Chapter 2 -- We apply both distance-based and kernel-based mutual dependence measures to independent component analysis (ICA), and generalize dCovICA to MDMICA, minimizing empirical dependence measures as an objective function in both deflation
요약Chapter 3 -- Independent component analysis (ICA) decomposes multivariate data into mutually independent components (ICs). The ICA model is subject to a constraint that at most one of these components is Gaussian, which is required for model ide
요약Chapter 4 -- A crucial problem in statistics is to decide whether additional variables are needed in a regression model. We propose a new multivariate test to investigate the conditional mean independence of Y given X conditioning on some known
일반주제명Statistics.
Computer science.
Mathematics.
언어영어
기본자료 저록Dissertation Abstracts International80-01B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000936

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