Robust novelty detection in the framework of a contamination neighbourhood. (1st January 2013)

• Record Type: Journal Article
• Authors: Utkin, Lev V.; Zhuk, Yulia A.
• Is Part Of: International journal of intelligent information and database systems. Volume 7:Number 3(2013)
• Page Start: 205
• Page End: 224
• Publication Date: 2013-01-01
• Subjects: machine learning -- novelty detection -- classification -- minimax strategy -- support vector machine -- SVM -- quadratic programming; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

A novelty detection robust model is studied in the paper. It is based on contaminated (robust) models which produce a set of probability distributions of data points instead of the empirical distribution. The minimax and minimin strategies are used to construct optimal separating functions. An algorithm for computing the optimal parameters of the novelty detection model is reduced to a finite number of standard SVM tasks with weighted data points. Experimental results with synthetic and some real data illustrate the proposed novelty detection robust model.