Image Reconstruction Error Minimization via Distribution Adaptation and Low-Rank Constraint

Document Type : Original Article

Authors

Faculty of IT & Computer Engineering, Urmia University of Technology, Urmia, Iran

Abstract

Visual domain adaptation aims to learn robust models for the test data by knowledge transferring from a training data where the training and test sets are from different distributions. Existing approaches attempt to solve domain shift problem with either adaptation across domains or performing low-rank constraints. In this paper, we propose a two-phases unsupervised approach referred as image reconstruction Error minimization via Distribution Adaptation and low-rank constraint (EDA), which benefits from both the distribution adaptation and the low-rank constraints to tackle distribution mismatch across domains. In the first phase, our proposed approach projects the training and test data onto a common subspace in which the marginal and conditional distribution differences of domains are minimized. Moreover, EDA benefits from domain invariant clustering to discriminate between various classes of data. In the second phase, for preserving data structure in the shared subspace, EDA minimizes the data reconstruction error using low-rank and sparse constraints. Overall, EDA solves the domain mismatch problem in cubic time complexity. The proposed approach is evaluated on variety of visual benchmark datasets and its performance is compared with the other state-of-the-art domain adaptation methods. The average accuracy of EDA on 32 experiments is determined 68.33% where outperforms other state-of-the-art domain adaptation methods with 4.28% improvement. 

Keywords

References

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