DocumentCode :
1470532
Title :
Sparse Kernel-Based Hyperspectral Anomaly Detection
Author :
Gurram, Prudhvi ; Kwon, Heesung ; Han, Timothy
Author_Institution :
U.S. Army Res. Lab., Adelphi, MD, USA
Volume :
9
Issue :
5
fYear :
2012
Firstpage :
943
Lastpage :
947
Abstract :
In this letter, a novel ensemble-learning approach for anomaly detection is presented. The proposed technique aims to optimize an ensemble of kernel-based one-class classifiers, such as support vector data description (SVDD) classifiers, by estimating optimal sparse weights of the subclassifiers. In this method, the features of a given multivariate data set representing normalcy are first randomly subsampled into a large number of feature subspaces. An enclosing hypersphere that defines the support of the normalcy data in the reproducing kernel Hilbert space (RKHS) of each respective feature subspace is estimated using standard SVDD. The joint hypersphere in the RKHS of the combined kernel is learned by optimally combining the weighted individual kernels while imposing the l1 constraint on the combining weights. The joint hypersphere representing the optimal compact support of the multivariate data in the joint RKHS is then used to test a new data point to determine if it belongs to the normalcy data or not. A performance comparison between the proposed algorithm and regular SVDD is reported using hyperspectral image data as well as general multivariate data.
Keywords :
Hilbert spaces; geophysical image processing; image classification; learning (artificial intelligence); support vector machines; RKHS; SVDD; enclosing hypersphere; ensemble-learning approach; feature subspaces; hyperspectral image data; joint hypersphere; kernel-based one-class classifier ensemble; reproducing kernel Hilbert space; sparse kernel-based hyperspectral anomaly detection; subclassifier optimal sparse weight estimation; support vector data description classifiers; Hyperspectral imaging; Joints; Kernel; Niobium; Support vector machines; Vectors; Hyperspectral anomaly detection; sparse kernel-based ensemble learning (SKEL); support vector data description (SVDD);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2012.2187040
Filename :
6170543
Link To Document :
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