Learning Representations Of Ultrahigh-dimensional Data For Random Distance-based Outlier Detection

Authors:
Guansong Pang University of Technology, Sydney
Longbing Cao Faculty of IT, University of Technology Sydney
Ling Chen University of Technology, Sydney
Huan Liu Arizona State University

Introduction:

This paper studies the problem of Learning expressive low-dimensional representations of ultrahigh-dimensional data.

Abstract:

Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).

You may want to know: