Learning ECOC and dichotomizers jointly from data

Authors:
Guoqiang Zhong;Kaizhu Huang;Cheng-Lin Liu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

In this paper, we present a first study which learns the ECOC matrix as well as dichotomizers simultaneously from data; these two steps are usually conducted independently in previous methods. We formulate our learning model as a sequence of concave-convex programming problems and develop an efficient alternative minimization algorithm to solve it. Extensive experiments over eight real data sets and one image analysis problem demonstrate the advantage of our model over other state-of-theart ECOC methods in multi-class classification.