| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Karampatziakis, N. & Mineiro, P. | Discriminative Features via Generalized Eigenvectors | 2013 | misc | URL | |
| Abstract: Representing examples in a way that is compatible with the underlying assifier can greatly enhance the performance of a learning system. In this per we investigate scalable techniques for inducing discriminative features taking advantage of simple second order structure in the data. We focus on lticlass classification and show that features extracted from the generalized genvectors of the class conditional second moments lead to classifiers with cellent empirical performance. Moreover, these features have attractive eoretical properties, such as inducing representations that are invariant to near transformations of the input. We evaluate classifiers built from these atures on three different tasks, obtaining state of the art results. |
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BibTeX:
@misc{karampatziakis2013discriminative,
author = {Karampatziakis, Nikos and Mineiro, Paul},
title = {Discriminative Features via Generalized Eigenvectors},
year = {2013},
note = {cite arxiv:1310.1934},
url = {http://arxiv.org/abs/1310.1934}
}
|
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