Title

Similarity Kernels For Nearest Neighbor-Based Outlier Detection

Keywords

Nearest neighbors; Outlier detection; Similarity kernels; Similarity scores

Abstract

Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighbor-based outlier detection. It introduces the class of similarity kernels and employs it within that framework. We also propose the use of isotropic stationary kernels for the case of normed input spaces. Two definitions of similarity scores using kernels are given: the k-NN kernel similarity score (kNNSS) and the summation kernel similarity score (SKSS). The paper concludes with preliminary experimental results comparing the performance of kNNSS and SKSS for outlier detection on four data sets. SKSS compared favorably to kNNSS. © 2010 Springer-Verlag Berlin Heidelberg.

Publication Date

6-25-2010

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

6065 LNCS

Number of Pages

159-170

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-13062-5_16

Socpus ID

77953739229 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953739229

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