Transfer subspace learning via low-rank and discriminative reconstruction matrix

Abstract

In this paper, we investigate the unsupervised domain transfer learning in which there is no label in the target samples whereas the source samples are all labeled. We use the transformation matrix to transfer both target and source samples to a common subspace where they have the same distribution and each target sample in the transformed space is constructed of a linear combination of the source samples. To preserve the local and global structure of the samples in the transferred domain, the low-rank and sparse constraints are imposed on the reconstruction coefficient matrix. In this paper, in order to consider the discriminative ability of the target and source samples, the information content of the reconstruction coefficient matrix is utilized. To capture the discriminative ability of the target samples, it is assumed that the class labels of the source samples which are linearly incorporated in constructing a target sample should be the same. Based on this assumption, it is assured that the target samples are well distributed over the transferred domain. To handle this, we utilize the linear entropy to measure the discriminant power of the target domain. This term considers the discriminative ability of the target samples without using their hidden labels. Also, to assess the discriminative ability of source samples, we use max-margin classifier where the kernel matrix is defined by using the reconstruction coefficient matrix. To evaluate the proposed approach, it is applied on MSRC, VOC 2007, CMU PIE, Office, Caltech-256, Extended Yale B and two imbalanced datasets. The experimental results show that our proposed approach outperforms its competitors.