Totally-corrective multi-class boosting

Authors:
Zhihui Hao;Chunhua Shen;Nick Barnes;Bo Wang
Affiliations:
Beijing Institute of Technology, Beijing, China;NICTA, Canberra Research Laboratory and Australian National University, Canberra, Australia;NICTA, Canberra Research Laboratory and Australian National University, Canberra, Australia;Beijing Institute of Technology, Beijing, China
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Year:
2010

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Abstract

We proffer totally-corrective multi-class boosting algorithms in this work. First, we discuss the methods that extend two-class boosting to multi-class case by studying two existing boosting algorithms: AdaBoost.MO and SAMME, and formulate convex optimization problems that minimize their regularized cost functions. Then we propose a column-generation based totally-corrective framework for multi-class boosting learning by looking at the Lagrange dual problems. Experimental results on UCI datasets show that the new algorithms have comparable generalization capability but converge much faster than their counterparts. Experiments on MNIST handwriting digit classification also demonstrate the effectiveness of the proposed algorithms.