Publication 2009 Issue No. 11 - November Abstract - A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
November 2009 (vol. 31 no. 11)
pp. 2088-2092
 ASCII Text x Mingrui Wu, Jieping Ye, "A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2088-2092, November, 2009.
 BibTex x @article{ 10.1109/TPAMI.2009.24,author = {Mingrui Wu and Jieping Ye},title = {A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {31},number = {11},issn = {0162-8828},year = {2009},pages = {2088-2092},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.24},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with OutliersIS - 11SN - 0162-8828SP2088EP2092EPD - 2088-2092A1 - Mingrui Wu, A1 - Jieping Ye, PY - 2009KW - Novelty detectionKW - one-class classificationKW - support vector machineKW - kernel methods.VL - 31JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -
Mingrui Wu, Yahoo! Inc., Sunnyvale
Jieping Ye, Arizona State University, Tempe
We present a small sphere and large margin approach for novelty detection problems, where the majority of training data are normal examples. In addition, the training data also contain a small number of abnormal examples or outliers. The basic idea is to construct a hypersphere that contains most of the normal examples, such that the volume of this sphere is as small as possible, while at the same time the margin between the surface of this sphere and the outlier training data is as large as possible. This can result in a closed and tight boundary around the normal data. To build such a sphere, we only need to solve a convex optimization problem that can be efficiently solved with the existing software packages for training \nu\hbox{-}Support Vector Machines. Experimental results are provided to validate the effectiveness of the proposed algorithm.

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Index Terms:
Novelty detection, one-class classification, support vector machine, kernel methods.
Citation:
Mingrui Wu, Jieping Ye, "A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2088-2092, Nov. 2009, doi:10.1109/TPAMI.2009.24