Outlying sequence detection in large data sets: A data-driven approach

Ali Tajer, Venugopal V. Veeravalli, H. Vincent Poor

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

Outliers refer to observations that do not conform to the expected patterns in high-dimensional data sets. When such outliers signify risks (e.g., in fraud detection) or opportunities (e.g., in spectrum sensing), harnessing the costs associated with the risks or missed opportunities necessitates mechanisms that can identify them effectively. Designing such mechanisms involves striking an appropriate balance between reliability and cost of sensing, as two opposing performance measures, where improving one tends to penalize the other. This article poses and analyzes outlying sequence detection in a hypothesis testing framework under different outlier recovery objectives and different degrees of knowledge about the underlying statistics of the outliers.

Original languageEnglish (US)
Article number6879597
Pages (from-to)44-56
Number of pages13
JournalIEEE Signal Processing Magazine
Volume31
Issue number5
DOIs
StatePublished - Sep 2014

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Outlying sequence detection in large data sets: A data-driven approach'. Together they form a unique fingerprint.

  • Cite this