Tensor machines for learning target-specific polynomial features

Jiyan Yang; Alex Gittens

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arXiv · 1504.01697

Tensor machines for learning target-specific polynomial features

Jiyan Yang·Alex Gittens

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Abstract

Recent years have demonstrated that using random feature maps can significantly decrease the training and testing times of kernel-based algorithms without significantly lowering their accuracy. Regrettably, because random features are target-agnostic, typically thousands of such features are necessary to achieve acceptable accuracies. In this work, we consider the problem of learning a small number of explicit polynomial features. Our approach, named Tensor Machines, finds a parsimonious set of features by optimizing over the hypothesis class introduced by Kar and Karnick for random feature maps in a target-specific manner. Exploiting a natural connection between polynomials and tensors, we provide bounds on the generalization error of Tensor Machines. Empirically, Tensor Machines behave favorably on several real-world datasets compared to other state-of-the-art techniques for learning polynomial features, and deliver significantly more parsimonious models.

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Jiyan Yang, Alex Gittens. 2015-04-07. Tensor machines for learning target-specific polynomial features. https://arxiv.org/abs/1504.01697

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