Multi-Label Prediction via Compressed Sensing

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Multi-Label Prediction via Compressed Sensing (902)

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by Daniel Hsu , Sham M. Kakade , John Langford , Tong Zhang

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100 - 3 self

BibTeX

@MISC{Hsu902multi-labelprediction,
    author = {Daniel Hsu and Sham M. Kakade and John Langford and Tong Zhang},
    title = {Multi-Label Prediction via Compressed Sensing},
    year = {902}
}

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Abstract

We consider multi-label prediction problems with large output spaces under the assumption of output sparsity – that the target vectors have small support. We develop a general theory for a variant of the popular ECOC (error correcting output code) scheme, based on ideas from compressed sensing for exploiting this sparsity. The method can be regarded as a simple reduction from multilabel regression problems to binary regression problems. It is shown that the number of subproblems need only be logarithmic in the total number of label values, making this approach radically more efficient than others. We also state and prove performance guarantees for this method, and test it empirically. 1.

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