Year: 2013
Type: conference-paper
Abstract: Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that explores the quadratic entropy to adaptively select a set of neighboring instances, and a learning method to seek an optimal subspace in which an outlier is maximally separated from its neighbors. We show that this learning task can be solved via the matrix eigen-decomposition and its solution contains essential information to reveal features that are most important to interpret the exceptional properties of outliers. We demonstrate the appealing performance of LODI via a number of synthetic and real world datasets and compare its outlier detection rates against state-of-the-art algorithms. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Institutions: Aarhus University, University of British Columbia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Danmarks Frie Forskningsfond
OpenAlex ID: W112228497