Training boosting-like algorithms with semi-supervised subspace learning

Publication Type:
Conference Proceeding
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 4302 - 4306
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Boosting algorithms have attracted great attention since the first real-time face detector by Viola & Jones through feature selection and strong classifier learning simultaneously. On the other hand, researchers have proposed to decouple such two procedures to improve the performance of Boosting algorithms. Motivated by this, we propose a boosting-like algorithm framework by embedding semi-supervised subspace learning methods. It selects weak classifiers based on class-separability. Combination weights of selected weak classifiers can be obtained by subspace learning. Three typical algorithms are proposed under this framework and evaluated on public data sets. As shown by our experimental results, the proposed methods obtain superior performances over their supervised counterparts and AdaBoost. © 2013 IEEE.
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