Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions

Jun Hong Wang; Yun Qian Miao; Alaa Khamis; Fakhri Karray; Jiye Liang

doi:10.1007/978-3-319-41501-7_1

Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions

Jun Hong Wang^*
, Yun Qian Miao
, Alaa Khamis
, Fakhri Karray
, Jiye Liang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings
Editors	Aurelio Campilho, Aurelio Campilho, Fakhri Karray
Publisher	Springer Verlag
Pages	3-11
Number of pages	9
ISBN (Print)	9783319415000
DOIs	https://doi.org/10.1007/978-3-319-41501-7_1
State	Published - 2016
Externally published	Yes
Event	13th International Conference on Image Analysis and Recognition, ICIAR 2016 - Povoa de Varzim, Portugal Duration: 13 Jul 2016 → 16 Jul 2016

Publication series

Name	Lecture Notes in Computer Science
Volume	9730
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Image Analysis and Recognition, ICIAR 2016
Country/Territory	Portugal
City	Povoa de Varzim
Period	13/07/16 → 16/07/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Adaptation mechanisms
Clustering
Domain adaptation
Novelty detection
Unsupervised learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-41501-7_1

Cite this

Wang, J. H., Miao, Y. Q., Khamis, A., Karray, F., & Liang, J. (2016). Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions. In A. Campilho, A. Campilho, & F. Karray (Eds.), Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings (pp. 3-11). (Lecture Notes in Computer Science; Vol. 9730). Springer Verlag. https://doi.org/10.1007/978-3-319-41501-7_1

Wang, Jun Hong ; Miao, Yun Qian ; Khamis, Alaa et al. / Adaptation approaches in unsupervised learning : A survey of the state-of-the-art and future directions. Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. editor / Aurelio Campilho ; Aurelio Campilho ; Fakhri Karray. Springer Verlag, 2016. pp. 3-11 (Lecture Notes in Computer Science).

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title = "Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions",

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.",

keywords = "Adaptation mechanisms, Clustering, Domain adaptation, Novelty detection, Unsupervised learning",

author = "Wang, \{Jun Hong\} and Miao, \{Yun Qian\} and Alaa Khamis and Fakhri Karray and Jiye Liang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 13th International Conference on Image Analysis and Recognition, ICIAR 2016 ; Conference date: 13-07-2016 Through 16-07-2016",

year = "2016",

doi = "10.1007/978-3-319-41501-7\_1",

language = "English",

isbn = "9783319415000",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

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Wang, JH, Miao, YQ, Khamis, A, Karray, F & Liang, J 2016, Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions. in A Campilho, A Campilho & F Karray (eds), Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. Lecture Notes in Computer Science, vol. 9730, Springer Verlag, pp. 3-11, 13th International Conference on Image Analysis and Recognition, ICIAR 2016, Povoa de Varzim, Portugal, 13/07/16. https://doi.org/10.1007/978-3-319-41501-7_1

Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions. / Wang, Jun Hong; Miao, Yun Qian; Khamis, Alaa et al.
Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. ed. / Aurelio Campilho; Aurelio Campilho; Fakhri Karray. Springer Verlag, 2016. p. 3-11 (Lecture Notes in Computer Science; Vol. 9730).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Adaptation approaches in unsupervised learning

T2 - 13th International Conference on Image Analysis and Recognition, ICIAR 2016

AU - Wang, Jun Hong

AU - Miao, Yun Qian

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AU - Karray, Fakhri

AU - Liang, Jiye

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

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AB - 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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Wang JH, Miao YQ, Khamis A, Karray F, Liang J. Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions. In Campilho A, Campilho A, Karray F, editors, Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. Springer Verlag. 2016. p. 3-11. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-41501-7_1

Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this