Multi-view feature learning based on user contributed tag

A multi-view feature learning method based on user contributed tag was proposed. Bag-of-words representation for content feature and textual feature was learned. A multi-view feature learning framework was proposed to explicitly model the relevance between multimedia object and tags by learning a linear mapping from textual representation to visual representation. The learned feature encoded the information conveyed by original feature, and inner products of leaned features were preserved with a high probability with visual features and textual features. The complexity of the method is linear with respect to the size of dataset. Furthermore, the method can be extended to deal with more than two views. The performance of the proposed method indicts it's superiority over other representative method.