Scalable multi-label canonical correlation analysis for cross-modal retrieval

Multi-label canonical correlation analysis (ml-CCA) has been developed for cross-modal retrieval. However, the computation of ml-CCA involves dense matrices eigendecomposition, which can be computationally expensive. In addition, only takes semantic into account ignores feature correlation. this paper, we propose a novel framework to simultaneously integrate and By using transformation, show that our model avoid computing covariance matrix explicitly is huge save computational cost. Further shows proposed method solved via singular value decomposition linear time complexity. Experimental results on three multi-label datasets have demonstrated accuracy efficiency method.