Adaptive Mixture Discriminant Analysis for Supervised Learning with Unobserved Classes

Charles Bouveyron ()

Journal of Classification, 2014, vol. 31, issue 1, 49-84

Abstract: In supervised learning, an important issue usually not taken into account by classical methods is that a class represented in the test set may have not been encountered earlier in the learning phase. Classical supervised algorithms will automatically label such observations as belonging to one of the known classes in the training set and will not be able to detect new classes. This work introduces a model-based discriminant analysis method, called adaptive mixture discriminant analysis (AMDA), which can detect several unobserved groups of points and can adapt the learned classifier to the new situation. Two EM-based procedures are proposed for parameter estimation and model selection criteria are used for selecting the actual number of classes. Experiments on artificial and real data demonstrate the ability of the proposed method to deal with complex and real-world problems. The proposed approach is also applied to the detection of unobserved communities in social network analysis. Copyright Springer Science+Business Media New York 2014

Keywords: Supervised classification; Unobserved classes; Adaptive learning; Multiclass novelty detection; Model-based classification; Social network analysis (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00357-014-9147-x (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:31:y:2014:i:1:p:49-84

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-014-9147-x

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().