Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/69851
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Type: Conference paper
Title: Sharing features in multi-class boosting via group sparsity
Author: Paisitkriangkrai, S.
Shen, C.
Van Den Hengel, A.
Citation: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), held in Rhode Island, USA, 16-21 June 2012: pp. 2128-2135
Publisher: IEEE
Publisher Place: USA
Issue Date: 2012
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781467312264
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (25th : 2012 : Providence, Rhode Island)
Statement of
Responsibility: 
Sakrapee Paisitkriangkrai, Chunhua Shen and Anton van den Hengel
Abstract: We present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features. Our multi-class boosting is solved in a single optimization problem. In order to share features across different classes, we introduce the mixed-norm regularization, which promotes group sparsity, into boosting. We then derive the Lagrange dual problems which enable us to design fully corrective multi-class algorithms using the primal-dual optimization technique. We show that sharing features across classes can improve classification performance and efficiency. We empirically show that in many cases, the proposed multi-class boosting generalizes better than a range of competing multi-class boosting algorithms due to the capability of feature sharing. Experimental results on machine learning data, visual scene and object recognition demonstrate the efficiency and effectiveness of proposed algorithms and validate our theoretical findings.
Keywords: boosting; multi-class classification; feature sharing; column generation; convex optimization
Rights: Copyright IEEE
RMID: 0020122223
DOI: 10.1109/CVPR.2012.6247919
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6235193
Appears in Collections:Computer Science publications

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