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Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation

Yuguang Yan, Wen Li, Michael Ng, Mingkui Tan, Hanrui Wu, Huaqing Min, and Qingyao Wu
International Joint Conference on Artificial Intelligence (IJCAI)
August 2017

Abstract

Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.


Link to publisher's page
@InProceedings{eth_biwi_01392,
  author = {Yuguang Yan and Wen Li and Michael Ng and Mingkui Tan and Hanrui Wu and Huaqing Min and and Qingyao Wu},
  title = {Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2017},
  month = {August},
  pages = {3252-3258},
  keywords = {}
}