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Fuzzy Multiple Subspace Fitting for Anomaly Detection
Raissa RELATOR Tsuyoshi KATO Takuma TOMARU Naoya OHTA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2014/10/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
fuzzy algorithm, subspace fitting, kernel vector subspace, kernel affine subspace, anomaly detection,
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Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.