Robust latent semantic exploration for image retrieval in social media

With the speedy development of social media, more and more multimedia data are generated by users with tags associated. The tag information provides the extra cue to link multimedia data in addition to the multimedia content itself. However, the manually added tags are always with noise and not correct enough. Moreover, the semantically similar tags exist massively but cannot be accounted for well. This paper proposes a new algorithm to robustly combine multimedia content and associated tags by mining the latent semantic which takes into account the semantically similar tags. The l2,1 norm is proposed to employ in latent semantic indexing for a more robust latent space, and a word-to-vector based clustering method is proposed to address the massive tags with similar meaning. The experiments on extensive data demonstrate the proposed method. Compared to the existing latent semantic based methods, the algorithm proposed a more robust model to deal with noise.