Multi-View Synthesis and Analysis Dictionaries Learning for Classification

Fei WU  Xiwei DONG  Lu HAN  Xiao-Yuan JING  Yi-mu JI  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.3   pp.659-662
Publication Date: 2019/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8107
Type of Manuscript: LETTER
Category: Pattern Recognition
multi-view dictionary learning,  synthesis dictionary,  analysis dictionary,  uncorrelation term,  

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Recently, multi-view dictionary learning technique has attracted lots of research interest. Although several multi-view dictionary learning methods have been addressed, they can be further improved. Most of existing multi-view dictionary learning methods adopt the l0 or l1-norm sparsity constraint on the representation coefficients, which makes the training and testing phases time-consuming. In this paper, we propose a novel multi-view dictionary learning approach named multi-view synthesis and analysis dictionaries learning (MSADL), which jointly learns multiple discriminant dictionary pairs with each corresponding to one view and containing a structured synthesis dictionary and a structured analysis dictionary. MSADL utilizes synthesis dictionaries to achieve class-specific reconstruction and uses analysis dictionaries to generate discriminative code coefficients by linear projection. Furthermore, we design an uncorrelation term for multi-view dictionary learning, such that the redundancy among synthesis dictionaries learned from different views can be reduced. Two widely used datasets are employed as test data. Experimental results demonstrate the efficiency and effectiveness of the proposed approach.