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2016 | OriginalPaper | Buchkapitel

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

verfasst von : JunHong Wang, YunQian Miao, Alaa Khamis, Fakhri Karray, Jiye Liang

Erschienen in: Image Analysis and Recognition

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Adaptation Approaches in Unsupervised Learning: A Survey of the State-of-the-Art and Future Directions
verfasst von
JunHong Wang
YunQian Miao
Alaa Khamis
Fakhri Karray
Jiye Liang
Copyright-Jahr
2016
Buch
Image Analysis and Recognition

Print ISBN: 978-3-319-41500-0

Electronic ISBN: 978-3-319-41501-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-41501-7_1