2013/10/07 by Nikos Karampatziakis, Karampatziakis, Nikos, Paul Mineiro +1 · 1 citation
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Neural Networks and Applications #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1310.1934
arxiv created 2013/10/07 · openalex publication_date 2013/10/07 · arxiv updated 2013/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.