Robust multi-source adaptation visual classification using supervised low-rank representation. (January 2017) - Tao, JianWen; Song, Dawei; Wen, Shiting; Hu, Wenjun
Abstract: How to guarantee the robustness of multi-source adaptation visual classification is an important challenge in current visual learning community. To this end, we address in this paper the problem of robust visual classification with few labeled samples from the target domain of interest by leveraging multiple prior source models. Motivated by the recent success of low rank representation, we formulate this problem as a robust multi-source adaptation visual classification (RMAVC) model with supervised low rank representation by combining the strength of discriminative information from the target domain and the prior models from multiple source domains. Specifically, we propose a joint supervised low rank representation and multi-source adaptation visual classification framework, which achieves dual goals of finding the most discriminative low rank representation and multi-source adaptation classifier parameters for the target domain. While it is showed in this paper that the proposed RMAVC framework is effective and can produce high accuracy on several tasks of multi-source adaptation visual classification, this framework fails to consider the local geometrical structure of the target data and the heterogeneousness among multiple source domains. Hence, under this framework, we further present two effective extensions or variants, i.e., RMAVCK and RMAVC_FM, by exploiting multiple kernel trick and flexible manifold regularization, respectively. The proposed framework and its variants are robust for classifying visual objects accurately and the experimental results demonstrate the effectiveness of our methods on several types of image and video datasets. Highlights: Present a Robust Multi-source Adaptation framework using supervised low rank representation. Jointly optimize the supervised low rank representation and the adaptive classification model. We further propose two effective extensions. A generalization error bound is also derived for our extension. Comprehensive experiments verify the robustness and effectiveness of our methods.