Abstract
This paper proposes a novel robust latent common subspace learning (RLCSL) method by integrating low-rank and sparse constraints into a joint learning framework. Specifically, we transform the data from source and target domains into a latent common subspace to perform the data reconstruction, i.e., the transformed source data is used to reconstruct the transformed target data. We impose joint low-rank and sparse constraints on the reconstruction coefficient matrix which can achieve following objectives: (1) the data from different domains can be interlaced by using the low-rank constraint; (2) the data from different domains but with the same label can be aligned together by using the sparse constraint. In this way, the new feature representation in the latent common subspace is discriminative and transferable. To learn a suitable classifier, we also integrate the classifier learning and feature representation learning into a unified objective and thus the high-level semantics label (data label) is fully used to guide the learning process of these two tasks. Experiments are conducted on diverse data sets for image, object, and document classifications, and encouraging experimental results show that the proposed method outperforms some state-of-the-arts methods.
Highlights
Collecting massive labeled data is an expensive and time-consuming process in realistic scenarios [1]
In order to evaluate the validity of the proposed robust latent common subspace learning (RLCSL) method with different configurations of these data sets, we compared RLCSL with some competitive state-of-the-art methods including geodesic flow kernel (GFK) + NN [50], transfer component analysis (TCA) + NN [7], transfer subspace learning (TSL) + NN [42], low-rank transfer subspace learning (LDA) + NN (LTSL) [2], robust visual domain adaptation with low-rank reconstruction (RDALR) + NN [4], transfer feature learning with joint distribution adaptation (JDA) [53], scatter component analysis (SCA) [54], discriminative transfer subspace learning via low-rank and sparse representation (DTSL) [55], joint feature selection and subspace learning (FSSL) [56], transfer joint matching (TJM) for unsupervised domain adaptation [52], and 1-nearest neighbor classification (NN) and principle component analysis (PCA) + NN
This paper proposes a novel robust latent common subspace learning method which utilizes the joint low-rank and sparse constraints to constrain the reconstruction coefficient matrix for obtaining a transferable and discriminative feature representation
Summary
Collecting massive labeled data is an expensive and time-consuming process in realistic scenarios [1]. This paper proposes a novel transfer learning method called robust latent common subspace learning (RLCSL) by finding a proper data feature representation that can reduce the distribution discrepancy and improve the discriminative ability of the new feature representation. In this way, the new feature representation is beneficial for the subsequent learning task, i.e., classifier learning. By imposing a sparse constraint on reconstruction coefficient matrix, the data from different domains but with the same label can be aligned together for learning a discriminative feature representation.
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