Abstract
Canonical correlation analysis is a classical cross-modal correlation feature fusion method. Its discriminant-guided and structure-based variations have been proposed. However, the variations suffer from insufficient supervisory information and the weak robustness of structure distortion. Thus, we propose a semi-discriminant cross-modal correlation feature fusion method with structure elasticity, i.e. semi-discriminant elasticity canonical correlation analysis. In the method, we construct the structure elasticity by incorporating neighbor geometry manifolds while enforcing the global property of Euclidean distances. Besides, a small number of class labels can be effectively employed to constrain the construction of the structure elasticity and improve the discriminative power of elasticity correlation features. The method solves the problem of insufficient supervisory information and enhances the robustness of structure distortion in correlation fusion theories. Extensive experiments validate the effectiveness of the method in image recognition tasks.
Published Version
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