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A Unified Framework of Robust PCA: Use of Robust Unit Approach
로버스트 유닛을 통한 로버스트 주성분 분석

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Authors
김정음
Advisor
오희석
Major
자연과학대학 통계학과
Issue Date
2017-08
Publisher
서울대학교 대학원
Keywords
Missing valuesOutliersPrincipal component analysisProbabilistic PCARobust PCARobust unitSkewed data
Description
학위논문 (석사)-- 서울대학교 대학원 자연과학대학 통계학과, 2017. 8. 오희석.
Abstract
In this paper, we propose a new framework of robust PCA for improving the robustness and for reflecting various outlier types as well as skewed data. This framework is composed of two concepts: robust unit that is induced by a combination of any PCA procedure and a restriction function as an outlier filter and two-stage strategy that divides and conquers outliers. As a practical implementation of the proposed framework, we develop a robust PCA procedure, termed robust pair PCA (RP-PCA) by coupling a t-distribution-based probabilistic PCA (T-PCA) with our framework. Moreover, for missing data, we suggest a new procedure for handling missing values that fully exploits EM algorithm of T-PCA under the robust unit. Empirical performance of the proposed method is evaluated through numerical studies including simulation study and real data analysis, which demonstrates promising results of the proposed robust method.
Language
English
URI
http://hdl.handle.net/10371/138090
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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