The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A review on robust principal component analysis

강건 주성분분석에 대한 요약

  • Lee, Eunju (Department of Statistics, Pusan National University) ;
  • Park, Mingyu (Department of Statistics, Pusan National University) ;
  • Kim, Choongrak (Department of Statistics, Pusan National University)
  • Received : 2021.12.23
  • Accepted : 2022.01.17
  • Published : 2022.04.30
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Abstract

Principal component analysis (PCA) is the most widely used technique in dimension reduction, however, it is very sensitive to outliers. A robust version of PCA, called robust PCA, was suggested by two seminal papers by Candès et al. (2011) and Chandrasekaran et al. (2011). The robust PCA is an essential tool in the artificial intelligence such as background detection, face recognition, ranking, and collaborative filtering. Also, the robust PCA receives a lot of attention in statistics in addition to computer science. In this paper, we introduce recent algorithms for the robust PCA and give some illustrative examples.

차원 축소를 위한 통계적 방법중에 주성분분석이 가장 널리 사용되고 있으나 주성분 분석의 여러 가지 장점에도 불구하고 이상치에 매우 민감하여 이를 강건화 하기 위한 여러 가지 방법이 제시되었다. 그 중에서도 Candès 등 (2011)과 Chandrasekaran 등 (2011)이 제안한 강건 주성분분석이 계산 가능하며 가장 효율적인 방법으로 알려져 있으며 최근 비디오 감시, 안면인식 등의 인공지능분야에 많이 사용되고 있다. 본 논문에서는 강건 주성준 분석의 개념과 최근 제안된 가장 효율적인 알고리즘을 소개한다. 아울러 실제 자료에 근거한 예제를 소개하고 향후 연구분야도 제안한다.

Keywords

Acknowledgement

본 연구는 부산대학교 2년 과제 연구비에 의하여 수행되었음.

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