Datum-Wise Classification: A Sequential Approach to Sparsity

Details Datum-Wise Classification: A Sequential Approach to Sparsity Datum-Wise Classification: A Sequential Approach to Sparsity

2011-08-29

arxiv.org/abs/1108.5668v1

Lecture Notes in Computer Science, 2011, Volume 6911/2011, 375-390

Computer Science

Artificial Intelligence

ECML2011

Scientific paper

10.1007/978-3-642-23780-5_34

We propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation encompassing the whole dataset. This datum-wise representation is found by using a sparsity inducing empirical risk, which is a relaxation of the standard L 0 regularized risk. The classification problem is modeled as a sequential decision process that sequentially chooses, for each datapoint, which features to use before classifying. Datum-Wise Classification extends naturally to multi-class tasks, and we describe a specific case where our inference has equivalent complexity to a traditional linear classifier, while still using a variable number of features. We compare our classifier to classical L 1 regularized linear models (L 1-SVM and LARS) on a set of common binary and multi-class datasets and show that for an equal average number of features used we can get improved performance using our method.

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