Heterogeneous domain adaptation seeks to learn an effective classifier or regression model for unlabeled target samples by using the well-labeled source samples but residing in different feature spaces and lying different distributions. Most recent works have concentrated on learning domain-invariant feature representations to minimize the distribution divergence via target pseudo-labels. However, two critical issues need to be further explored: 1) new feature representations should be not only domain-invariant but also category-correlative and discriminative and 2) alleviating the negative transfer caused by the incorrect pseudo-labeling target samples could boost the adaptation performance during the iterative learning process. To address these issues, in this paper, we put forward a novel heterogeneous domain adaptation method to learn category-correlative and discriminative representations, referred to as correlative and discriminative feature learning (CDFL). Specifically, CDFL aims to learn a feature space where class-specific feature correlations between the source and target domains are maximized, the divergences of marginal and conditional distribution between the source and target domains are minimized, and the distances of inter-class distribution are forced to be maximized to ensure the discriminative ability. Meanwhile, a selective pseudo-labeling procedure based on the correlation coefficient and classifier prediction is introduced to boost class-specific feature correlation and discriminative distribution alignment in an iteration way. Extensive experiments certify that CDFL outperforms the State-of-the-Art algorithms on five standard benchmarks.

In this paper, we present a novel approach named correlation-guided distribution and geometry alignments (CDGA) for heterogeneous domain adaptation. Unlike existing methods that typically combine feature alignment and domain alignment into a single objective function, our proposed CDGA separates the two alignments into distinct steps. The two adaptation steps are: paired canonical correlation analysis (PCCA) and distribution and geometry alignments (DGA). In the PCCA step, CDGA focuses on maximizing the within-category correlation between source and target samples to produce the dimension-aligned feature representations for the next adaptation step. In the DGA step, CDGA is responsible for learning a classifier that incorporates both distribution and geometry alignments. Furthermore, during this step, the highly confident pseudo labeled samples are carefully selected for the next iteration of PCCA, establishing a beneficial coupling between PCCA and DGA to improve the adaptation performance in an iterative manner. Experimental results on various visual cross-domain benchmarks demonstrate that CDGA achieves remarkable performance compared to the existing shallow heterogeneous domain adaptation methods and even exhibits superiority over the state-of-the-art neural network-based approaches.