Joint Information Preservation for Heterogeneous Domain Adaptation

TL;DR

This paper introduces a joint information preservation approach for heterogeneous domain adaptation, effectively utilizing paired samples and structural data information to improve adaptation performance across different feature spaces.

Contribution

The paper proposes a novel method that preserves paired sample correlation and structural information, enhancing heterogeneous domain adaptation performance.

Findings

Outperforms state-of-the-art HDA algorithms

Effectively preserves original data information during adaptation

Utilizes paired samples and structural information comprehensively

Abstract

Domain adaptation aims to assist the modeling tasks of the target domain with knowledge of the source domain. The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA). A core issue of HDA is how to preserve the information of the original data during adaptation. In this paper, we propose a joint information preservation method to deal with the problem. The method preserves the information of the original data from two aspects. On the one hand, although paired samples often exist between the two domains of the HDA, current algorithms do not utilize such information sufficiently. The proposed method preserves the paired information by maximizing the correlation of the paired samples in the shared subspace. On the other hand, the proposed method improves the strategy of preserving the structural information of the original data, where the local and global structural information are preserved simultaneously. Finally, the joint information preservation is integrated by distribution matching. Experimental results show the superiority of the proposed method over the state-of-the-art HDA algorithms.