Robust Latent Regression with discriminative regularization by leveraging auxiliary knowledge

Neural Networks : the Official Journal of the International Neural Network Society
Jianwen TaoBin Zhu

Abstract

For a domain adaptation learning problem, how to minimize the distribution mismatch between different domains is one of key challenges. In real applications, it is reasonable to obtain an optimal latent space for both domains so as to reduce the domain distribution discrepancy as much as possible. We therefore propose in this paper a Robust Latent Regression (RLR) framework with discriminative regularization by uncovering a compact and more informative latent space as well as leveraging the source domain knowledge, which learns a discriminative representation of domain data by considering the recognition task in the procedure of domain adaptation learning. On the one hand, to leverage the prior information in the source domain, RLR incorporates both the source and target classification loss functions as parts of its objective function, and simultaneously trains these two classifiers by encoding the common components of the classifier models as a low-rank regularization term, thus exploiting the discriminative information shared by different domains. On the other hand, to guarantee that the latent space is more compact and discriminative, the intrinsic geometric structure of data, and the local and global structural consistencie...Continue Reading

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