A Copula Based Unsupervised Domain Adaptation for Image Classification

• 영문명: A Copula Based Unsupervised Domain Adaptation for Image Classification
• 발행기관: 한국자료분석학회
• 저자명: Seungmin Lee Kyupil Yeon
• 간행물 정보: 『한국자료분석학회 학술대회자료집』2023년 동계학술대회 발표집, 69~74쪽, 전체 6쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2023.12.31

국문 초록

In this paper, we propose an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The method is similar to the CORAL algorithm which aligns the correlation structure between source and target domains, but different in that it applies correlation alignment in copula spaces instead of the original variable spaces. Since a copula function enables us to analyze separately the dependency structure from the marginal distributions, the proposed algorithm is considered to be robust to a severely skewed characteristic of the marginals that can distort the correlation structure among the variables. We compared several feature level domain adaptation algorithms for image classification using office-caltech10 data set, and verified the proposed method showed better classification accuracy in an unsupervised domain adaptation framework.

키워드

copula function correlation alignment domain adaptation image classification PCA