Conference proceeding
Impact of sample size on false alarm and missed detection rates in PCA-based anomaly detection
2017 51st Annual Conference on Information Sciences and Systems (CISS), pp 1-6
Mar 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Principal component analysis (PCA) is widely used for anomaly detection, specifically by computing the distances of each point in the dataset to the (sample) subspace spanned by the principal components of the sample covariance matrix. Points with distances above a threshold are labeled as anomalous. Although it is typically unknown in practice, the distribution from which the sample is drawn has its own (population) subspace, spanned by the principal components of the distribution's covariance matrix, and the thresholded distances of each point in the dataset from this subspace determine the true labels of the points. It follows that the sample covariance matrix produces false alarms (points labeled anomalous that are not) and missed detections (points labeled non-anomalous that are). In this paper we study how the false alarm rate (FAR) and missed detection rate (MDR) depend upon the number of samples, focusing on the case where the sample points are drawn from a normal distribution with a covariance matrix having a single spike.
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7 citations in Scopus
Details
- Title
- Impact of sample size on false alarm and missed detection rates in PCA-based anomaly detection
- Creators
- Ni An - Drexel University, Electrical and Computer EngineeringSteven Weber - Drexel University
- Publication Details
- 2017 51st Annual Conference on Information Sciences and Systems (CISS), pp 1-6
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- CNS-1228847 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000404163300097
- Scopus ID
- 2-s2.0-85020209664
- Other Identifier
- 991019170455504721
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- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic