Multiple Distribution Data Description Learning Method for Novelty Detection

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

13 Citations (Scopus)


Current data description learning methods for novelty detection such as support vector data description and small sphere with large margin construct a spherically shaped boundary around a normal data set to separate this set from abnormal data. The volume of this sphere is minimized to reduce the chance of accepting abnormal data. However those learning methods do not guarantee that the single spherically shaped boundary can best describe the normal data set if there exist some distinctive data distributions in this set. We propose in this paper a new data description learning method that constructs a set of spherically shaped boundaries to provide a better data description to the normal data set. An optimisation problem is proposed and solving this problem results in an iterative learning algorithm to determine the set of spherically shaped boundaries. We prove that the classification error will be reduced after each iteration in our learning method. Experimental results on 23 well-known data sets show that the proposed method provides lower classification error rates
Original languageEnglish
Title of host publicationThe 2011 International Joint Conference on Neural Networks (IJCNN 2011)
EditorsAli A Minai
Place of PublicationSan Jose, USA
Number of pages6
ISBN (Electronic)9781424496372
ISBN (Print)9781424496358
Publication statusPublished - 31 Jul 2011
EventThe International Joint Conference on Neural Networks - San Jose, San Jose, United States
Duration: 31 Jul 20115 Aug 2011


ConferenceThe International Joint Conference on Neural Networks
Abbreviated titleIJCNN
Country/TerritoryUnited States
CitySan Jose


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