AdaBoost on low-rank PSD matrices for metric learning

AdaBoost on low-rank PSD matrices for metric learning,10.1109/CVPR.2011.5995363,Jinbo Bi,Dijia Wu,Le Lu,Meizhu Liu,Yimo Tao,Matthias Wolf

AdaBoost on low-rank PSD matrices for metric learning  
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The problem of learning a proper distance or similarity metric arises in many applications such as content-based image retrieval. In this work, we propose a boosting algorithm, MetricBoost, to learn the distance metric that preserves the proximity relationships among object triplets: objecti is more similar to objectj than to objectk. MetricBoost constructs a positive semi-definite (PSD) matrix that parameterizes the distance metric by combining rank-one PSD matrices. Different options of weak models and combination coefficients are derived. Unlike existing proximity preserving metric learning which is generally not scalable, MetricBoost employs a bipartite strategy to dramatically reduce computation cost by decomposing proximity relationships over triplets into pair-wise constraints. MetricBoost outperforms the state-of-the-art on two real-world medical problems: 1. identifying and quantifying diffuse lung diseases; 2. colorectal polyp matching between different views, as well as on other benchmark datasets.
Conference: Computer Vision and Pattern Recognition - CVPR , pp. 2617-2624, 2011
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