Feature selection focused within error clusters

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Feature selection focused within error clusters
We propose a feature selection method that constructs each new feature by analysis of tight error clusters. This is a greedy, time-efficient forward selection algorithm that iteratively constructs one feature at a time, until a desired error rate is reached. The algorithm finds error clusters in the current feature space, then projects one tight cluster into the null space of the feature mapping, where a new feature that helps to classify these errors can be discovered. Tight error clusters indicate that the current features are unable to discriminate these samples. The approach is strongly data-driven and restricted to linear features, but otherwise general. Large scale experiments show that it can achieve a monotonically decreasing error rate within the feature discovery set, and a generally decreasing error rate on a distinct test set.
Henry S. Baird, Sui-Yu Wang
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Henry S. Baird, Sui-Yu Wang
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