Nonnegative Matrix Factorization for Multimodality Data from Multi-source Domain

With the growing popularity of social tagging, more and more images are annotated by users on web sites(e.g., Flickr, Blogspace and Youtube). Since the tags annotated by users are often noisy, ambiguous, and subjective, it is beneficial to fully utilize the multiview information and borrow strength from multiple data sources to boost the performance of image annotation and tag-based image retrieval. Therefore, the appropriate integration and utilization of complimentary cues from multiple modality in multiple data sources is an important research topic. Inspired by the recent advances of multiview learning and shared subspace learning, this paper proposes an approach, namely Multimodality Multi-source Nonnegative Matrix Factorization (M2NMF) to learn a shared and corresponding individual structures from multimodality data via nonnegative matrix factorization. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.