Multi-view visual classification via a mixed-norm regularizer

Zhu, Xiaofeng, Huang, Zi and Wu, Xindong (2013). Multi-view visual classification via a mixed-norm regularizer. In: Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda and Guandong Xu, Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings. 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, Gold Coast, QLD, (520-531). 14 - 17 April 2013. doi:10.1007/978-3-642-37453-1_43

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Author Zhu, Xiaofeng
Huang, Zi
Wu, Xindong
Title of paper Multi-view visual classification via a mixed-norm regularizer
Conference name 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Conference location Gold Coast, QLD
Conference dates 14 - 17 April 2013
Proceedings title Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2013
Sub-type Fully published paper
DOI 10.1007/978-3-642-37453-1_43
Open Access Status
ISBN 9783642374524
9783642374531
ISSN 0302-9743
1611-3349
Editor Jian Pei
Vincent S. Tseng
Longbing Cao
Hiroshi Motoda
Guandong Xu
Volume 7818
Issue PART 1
Start page 520
End page 531
Total pages 12
Collection year 2014
Language eng
Abstract/Summary In data mining and machine learning, we often represent instances by multiple views for better descriptions and effective learning. However, such comprehensive representations can introduce redundancy and noise. Learning with these multi-view data without any preprocessing may affect the effectiveness of visual classification. In this paper, we propose a novel mixed-norm joint sparse learning model to effectively eliminate the negative effect of redundant views and noisy attributes (or dimensions) for multi-view multi-label (MVML) classification. In particular, a mixed-norm regularizer, integrating a Frobenius norm and an ℓ2,1-norm, is embedded into the framework of joint sparse learning to achieve the design goals, which include selecting significant views, preserving the intrinsic view structure and removing noisy attributes from the selected views. Moreover, we devise an iterative algorithm to solve the derived objective function of the proposed mixed-norm joint sparse learning model. We theoretically prove that the objective function converges to its global optimum via the algorithm. Experimental results on challenging real-life datasets show the superiority of the proposed learning model over state-of-the-art methods.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Keyword Feature selection
Joint sparse learning
Manifold learning
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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