Angelova, Anelia and Abu-Mostafa, Yaser S. and Perona, Pietro (2005) Pruning training sets for learning of object categories. In: Conference on Computer Vision and Pattern Recognition (CVPR '05),San Diego, CA, 20-25 June 2005. Vol.1. IEEE , Piscataway, NJ, pp. 494-501. ISBN 0-7695-2372-2 http://resolver.caltech.edu/CaltechAUTHORS:ANGcvpr05
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Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome for learning and exclude them from the training set. The problem is relevant to learning in semi-supervised or unsupervised setting, as well as to learning when the training data is contaminated with wrongly labeled examples or when correctly labeled, but hard to learn examples, are present. We propose a fully automatic mechanism for noise cleaning, called ’data pruning’, and demonstrate its success on learning of human faces. It is not assumed that the data or the noise can be modeled or that additional training examples are available. Our experiments show that data pruning can improve on generalization performance for algorithms with various robustness to noise. It outperforms methods with regularization properties and is superior to commonly applied aggregation methods, such as bagging.
|Item Type:||Book Section|
|Additional Information:||© Copyright 2005 IEEE. Reprinted with permission. Publication Date: 20-25 June 2005. Date Published in Issue: 2005-07-25. This research is supported by the NSF Center for Neuromorphic Systems Engineering grant EEC-9402726.|
|Subject Keywords:||face recognition; machine learning; object detection; aggregation method; data pruning; noise cleaning; object category learning; training set pruning|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Kristin Buxton|
|Deposited On:||08 Sep 2008 18:15|
|Last Modified:||26 Dec 2012 10:14|
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