Discriminative Features via Generalized Eigenvectors
This addresses the challenge of improving classifier compatibility in learning systems, though it appears incremental as it builds on existing second-order structure techniques.
The paper tackled the problem of enhancing multiclass classification performance by extracting discriminative features from generalized eigenvectors of class conditional second moments, achieving state-of-the-art results on three tasks.
Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classification and show that features extracted from the generalized eigenvectors of the class conditional second moments lead to classifiers with excellent empirical performance. Moreover, these features have attractive theoretical properties, such as inducing representations that are invariant to linear transformations of the input. We evaluate classifiers built from these features on three different tasks, obtaining state of the art results.