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Proceedings Paper

Anomaly-specified virtual dimensionality
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Paper Abstract

Virtual dimensionality (VD) has received considerable interest where VD is used to estimate the number of spectral distinct signatures, denoted by p. Unfortunately, no specific definition is provided by VD for what a spectrally distinct signature is. As a result, various types of spectral distinct signatures determine different values of VD. There is no one value-fit-all for VD. In order to address this issue this paper presents a new concept, referred to as anomaly-specified VD (AS-VD) which determines the number of anomalies of interest present in the data. Specifically, two types of anomaly detection algorithms are of particular interest, sample covariance matrix K-based anomaly detector developed by Reed and Yu, referred to as K-RXD and sample correlation matrix R-based RXD, referred to as R-RXD. Since K-RXD is only determined by 2nd order statistics compared to R-RXD which is specified by statistics of the first two orders including sample mean as the first order statistics, the values determined by K-RXD and R-RXD will be different. Experiments are conducted in comparison with widely used eigen-based approaches.

Paper Details

Date Published: 24 September 2013
PDF: 10 pages
Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 88710C (24 September 2013); doi: 10.1117/12.2027861
Show Author Affiliations
Shih-Yu Chen, Univ. of Maryland, Baltimore County (United States)
Drew Paylor, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 8871:
Satellite Data Compression, Communications, and Processing IX
Bormin Huang; Antonio J. Plaza; Chein-I Chang, Editor(s)

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