Fuzzy Multiple Subspace Fitting for Anomaly Detection

Raissa RELATOR  Tsuyoshi KATO  Takuma TOMARU  Naoya OHTA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.10   pp.2730-2738
Publication Date: 2014/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDP7027
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
fuzzy algorithm,  subspace fitting,  kernel vector subspace,  kernel affine subspace,  anomaly detection,  

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Summary: 
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.