Discriminative multi-view feature selection and fusion

In computer vision tasks such as action recognition and image classification, combining multiple visual feature sets is proven to be an effective strategy. However, simply combing these features may cause high dimensionality and lead to noises. Feature selection and fusion are common choices for multiple feature representation. In this paper, we propose a multi-view feature selection and fusion method which chooses and fuses discriminative features from multiple feature sets. For discriminative feature selection, we learn the selection matrix W by the minimization of the trace ratio objective function with ℓ_2,1 norm regularization. For multiple feature fusion, we incorporate local structures of each view in the Laplacian matrix. Since the Laplacian matrix is constructed in unsupervised manner and scaled category indicator matrix is solved iteratively, our work is fully unsupervised. Experimental results on four action recognition datasets and two large-scale image classification datasets demonstrate the effectiveness of multi-view feature selection and fusion.