Paper Info
Title
Supervised Novelty Detection
Abstract
In this paper we present a novel approach and a new machine learning problem, called Supervised Novelty Detection (SND). This problem extends the One-Class Support Vector Machine setting for binary classification while keeping the nice properties of novelty detection problem at hand. To tackle this we approach binary classification from a new perspective using two different estimators and a coupled regularization term. It involves optimization over a different objective and a doubled set of Lagrange multipliers. One might consider our approach as a joint estimation of the support for different probability distributions per class where an ultimate goal is to separate classes with the largest possible angle between the normal vectors to the decision hyperplanes in the feature space. Regarding an obvious novelty of our problem we report and compare the results along the lines of standard C-SVM, LS-SVM and One-Class SVM. Experiments have demonstrated promising results that validate the usefulness of the proposed method.
Year
DOI
Venue
2013
10.1109/CIDM.2013.6597229
Computational Intelligence and Data Mining
Keywords
Field
DocType
learning (artificial intelligence),optimisation,pattern classification,statistical distributions,support vector machines,vectors,Lagrange multiplier,binary classification,coupled regularization term,decision hyperplane,feature space,joint estimation,machine learning problem,normal vector,optimization,probability distribution,supervised novelty detection,support vector machine
Structured support vector machine,Novelty detection,One-class classification,Least squares support vector machine,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Novelty,Relevance vector machine,Linear classifier,Machine learning
Conference
Citations 
PageRank 
References 
3
0.38
13
Authors
2
Name
Order
Citations
PageRank
Vilen Jumutc130.38
Johan A. K. Suykens263553.51