Title :
Incorporating covariance information in one class support vector classification
Author :
Khan, N.M. ; Ksantiniy, Riadh ; Ahmad, Imran Shafiq ; Ling Guan
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
Unlike multi-class problems, the low variance directions in the training data are important for one-class classification. However, projecting in these directions before classification will result in loss of important data properties. This paper introduces a Covariance-guided One-Class Support Vector Machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM combines the global information from the covariance matrix of the training data with the local information of Support Vectors. Our proposed method is a convex optimization problem resulting in one global solution, which can be found efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM applications. Comparative experimental results with contemporary one-class classifiers on numerous benchmark datasets verify that our method results in significantly better performance.
Keywords :
covariance analysis; pattern classification; support vector machines; COSVM classification; covariance information; covariance-guided one-class support vector machine; multiclass problems; support vector classification; training data; Covariance matrices; Equations; Kernel; Optimization; Support vector machines; Training data; Vectors; Covariance; Outlier Detection; SVM;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638319