Computer Science ›› 2021, Vol. 48 ›› Issue (1): 233-240.doi: 10.11896/jsjkx.200800211

• Artificial Intelligence • Previous Articles     Next Articles

Multi-view Dictionary-pair Learning Based on Block-diagonal Representation

ZHANG Fan1,2,3, HE Wen-qi1,3, JI Hong-bing3, LI Dan-ping4, WANG Lei1,2,3   

  1. 1 Xidian University Qingdao Institute of Computing Technology,Qingdao,Shandong 266000,China
    2 Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China
    3 School of Electronic Engineering,Xidian University,Xi'an 710071,China
    4 School of Telecommunications Engineering,Xidian University,Xi'an 710071,China
  • Received:2020-08-31 Revised:2020-10-05 Online:2021-01-15 Published:2021-01-15
  • About author:ZHANG Fan,born in 1995,Ph.D student,is a member of China Compu-ter Federation.His main research interests include dictionary learning and multi-view learning.
    WANG Lei,born in 1979,associate professor,is a member of China Computer Federation.His main research interests include pattern recognition,signal & information processing,machine learning and computer vision.
  • Supported by:
    National Key Research and Development Program of China(2016YFE0207000),National Natural Science Foundation of China (61203137,61401328) and Natural Science Basic Research Program of Shaanxi Province of China(2014JQ8306, 2015JM6279).

Abstract: Dictionary learning is widely used in multi-view classification as an efficient feature learning technology.Most multi-view dictionary learning methods either only use part of the information of multi-view data,or only learn one type of dictionary in their frameworks.However,in practice,both the diversity information of multi-view data and the correlation of multi-view data are equally important.A single synthetic dictionary learning scheme or a single analytic dictionary learning scheme cannot meet the requirements of the processing speed,interpretability and application feasibility at the same time.To solve these issues,a novel block-diagonal representation based multi-view dictionary-pair learning framework (BDR-MVDPL) is proposed in this paper.This algorithm obtains the representation coefficients that contain more useful information for classification by introducing dictionary-pair learning model.Firstly,in order to ensure the discriminant ability of the coding coefficient matrix,the proposed me-thod directly enforces a block-diagonal constraint on the coding coefficients with explicit formulation.Then,it adopts a feature fusion strategy to concatenate the coding coefficients of different views,and regresses the concatenated coding coefficients to thecorresponding label vectors.In this way,both the diversity information of multi-view data and the correlation of multi-view data are considered.Finally,it integrates dictionary learning and classifier learning into a unified framework,so that the dictionary-pair and classifier can update alternately in an iterative manner and the whole classification task can be realized automatically.Experiments on several multi-feature datasets show that,compared to other multi-view dictionary learning algorithms,the proposed method achieves competitive performance in terms of classification accuracy,while enjoying a low computational complexity.

Key words: Dictionary learning, Dictionary-pair learning, Feature fusion, Multi-view learning

CLC Number: 

  • TP391
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