Sparsity induced prototype learning via [formula omitted]-norm grouping

Abstract

Prototype learning aims to eliminate redundancy of large-scale data by selecting an informative subset. It is at the center of visual data analysis and processing. However, due to intrinsic structures among sample groups, the learnt prototypes are generally less representative and diversified. To alleviate this issue, we develop in this paper a structurally regularized model via ℓp,1-norm grouping, in which both the intra-group and inter-group structures of source data in object-space are rationally exploited. Thus, while the learnt representative prototypes are prone to distribute in different groups at the inter-group level, the grouping constraint via ℓp,1-norm will enforce the greatest diversity for intra-group prototypes. Considering the convexity in the formulated model, an alternative re-weighting solver is presented to efficiently solve the proposed optimization problem. Experimental results on video summarization, scene categorization and handwriting recognition demonstrate that the proposed method is considerably superior to the state-of-the-art methods in prototype learning.