Mixed sparsity regularized multi-view unsupervised feature selection

The traditional learning machines suffer from the curse of dimensionality because of the data explosion in the areas of multi-media, social network, etc. Feature selection is an effective technique to reduce storage burden and time complexity, and improve generalization ability of the learned models. In real-world applications, the data can be collected from different modalities, or described from multi-views as well. Compared with supervised cases, it is more challenging to reduce the feature dimensionality of multi-view data in unsupervised circumstances. The key difficulty with multiview unsupervised feature selection is how to characterize the multi-view relationships. In this paper, we propose a novel method for multi-view unsupervised feature selection by imposing sparsity on both individual features and views. To exploit the complementary information, we also take the view importance into consideration without introducing explicit view weights. Experiments on benchmark datasets show on the proposed algorithm outperforms other unsupervised feature selection methods.