Jointly discriminative projection and dictionary learning for domain adaptive collaborative representation-based classification
作者：
Highlights：
• As far as we know, we are the first to apply CRC on domain adaptation problem. This paper proposed a jointly discriminative projection and dictionary learning method (JD2-CRC). The projection matrices and dictionary are learned according to the classification rule of CRC for improving the discriminability of collaborative representations.
• Different to traditional optimization algorithm where a ratio trace problem is solved, we exploit the relationship of optimal variables, and optimize the trace ratio problem directly by a new trace ratio optimization method which is efficient and effective.
• We conduct several experiments to verify the performances of the proposed algorithm. The experimental results show that the proposed optimization method is effective, and JD2-CRC achieves better performance than other state-of-the-art methods in domain adaptation problem.
摘要
•As far as we know, we are the first to apply CRC on domain adaptation problem. This paper proposed a jointly discriminative projection and dictionary learning method (JD2-CRC). The projection matrices and dictionary are learned according to the classification rule of CRC for improving the discriminability of collaborative representations.•Different to traditional optimization algorithm where a ratio trace problem is solved, we exploit the relationship of optimal variables, and optimize the trace ratio problem directly by a new trace ratio optimization method which is efficient and effective.•We conduct several experiments to verify the performances of the proposed algorithm. The experimental results show that the proposed optimization method is effective, and JD2-CRC achieves better performance than other state-of-the-art methods in domain adaptation problem.
论文关键词：Collaborative representation,Dimensionality reduction,Dictionary learning,Domain adptation
论文评审过程：Received 16 April 2018, Revised 17 November 2018, Accepted 4 January 2019, Available online 31 January 2019, Version of Record 6 February 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.004
