Feature Selection With Multi-Source Transfer

Feature selection aims at choosing a subset of features to represent the original feature space. In practice, however, it is hard to achieve desirable performance due to limited training data. To alleviate this issue, we propose a novel problem named feature selection with multi-source transfer where the privileged information from another data source or modality- only available during the training phase, is exploited to improve the performance of feature selection. To be exact, we propose a novel objective function that formulates the privileged information into feature selection. Moreover, an efficient optimization algorithm is introduced to solve the proposed problem of high dimension. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms several popular algorithms, especially when the training data size and the selected feature size are small.