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: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.26 No.2, 433~444쪽, 전체 12쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2024.04.30

영문 초록

In this paper, we present an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The motivation of the proposed algorithm stems from the idea of CORAL algorithm which extracts domain invariant features by aligning the correlation structure between a source and a target domain. However, it suffers from the fact that highly skewed marginal distributions happen to distort the correlation structure so that it may cause a negative transfer. Therefore we utilize a copula function that enables us to analyze separately the dependency structure and the marginals by Sklar’s theorem. In particular, we propose to align the Gaussian copula correlations in the copula feature spaces instead of aligning the correlation matrices in the original space. Considering the extremely skewed distribution of SURF image features in Office-Caltech10 data set we apply PCA first in order to extract some skewness-mitigated principal features and then derive copula features to align for domain adaptation by using CORAL idea with Gaussian copula correlation matrices. The proposed method showed a good classification accuracy when applied to image classification problem in an unsupervised domain adaptation setting.

목차

1. Introduction

2. Review of Domain Adaptation and Copula Theory

3. Copula Based Unsupervised Domain Adaptation

4. Data Analysis

5. Conclusions

References

키워드

copula function correlation alignment domain adaptation image classification PCA