Exploring Shared Subspace and Joint Sparsity for Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been extensively employed in various real-world applications of multi-label annotation. However, two major challenges are raised by the classical CCA. First, CCA frequently fails to remove noisy and irrelevant features. Second, CCA cannot effectively capture correlations between multiple labels, which are especially beneficial for multi-label learning. In this paper, we propose a novel framework that integrates joint sparsity and low-rank shared subspace into the least-squares formulation of CCA. Under this framework, multiple label interactions can be uncovered by the shared structure of the input features and a few highly discriminative features can be decided via structured sparsity inducing norm. Owing to the inclusion of the non-smooth row sparsity, a new efficient iterative algorithm is derived with proved convergence. The empirical studies on several popular web image and movie data collections consistently deliver the effectiveness of our new formulation in comparison with competing algorithms.