Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

JunHong Wang , YunQian Miao , Alaa Khamis , Fakhri Karray , Jiye Liang

ABSTRACT

In real applications, data continuously evolve over time and change from one setting to another. This inspires the development of adaptive learning algorithms to deal with this data dynamics. Adaptation mechanisms for unsupervised learning have received an increasing amount of attention from researchers. This research activity has produced a lot of results in tackling some of the challenging problems of the adaptation process that are still open. This paper is a brief review of adaptation mechanisms in unsupervised learning focusing on approaches recently reported in the literature for adaptive clustering and novelty detection and discussing some future directions. Although these approaches have able to cope with different levels of data non-stationarity, there is a crucial need to extend these approaches to be able to handle large amount of data in distributed resource-limited environments. More... »

PAGES

3-11

References to SciGraph publications

Book

TITLE

Image Analysis and Recognition

ISBN

978-3-319-41500-0
978-3-319-41501-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-41501-7_1

DOI

http://dx.doi.org/10.1007/978-3-319-41501-7_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019518789


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