An Informative Logistic Regression for Cross-Domain Image Classification

Cross-domain image classification is a challenge problem in numerous practical applications and has attracted a lot of interests from research and industry communities. It differs from traditional closed set image classification due to the variance between the training and testing datesets. Although the semantics of the image categories are the same, the image variance between testing and training often results in significant loss of performance. To solve the problem, most previous works resort to data pre-processing approaches, such as minimizing the difference between the distributions of the training and testing datasets. In this paper, we propose a novel informative feature preserving classifier for cross-domain image classification. We introduce the idea of maximizing the variance of unlabeled training data into a L1 based logistic regression model, so that the informative features can be preserved in the model training which consequently leads to performance improvement in the testing. Experiments conducted on commonly used benchmarks for cross-domain image classification show that our method significantly outperforms the state-of-the-art.