Work

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

Year: 2018

Type: conference-paper

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).

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3219819.3220042

Authors: Guansong Pang, Longbing Cao, Ling Chen, Huan Liu

Institutions: University of Technology Sydney, Arizona State University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Centre of Excellence for Core to Crust Fluid Systems, Australian Research Council

Award: G5034118890