Semi-supervised deep embedded clustering

Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the...

Full description

Saved in:
Bibliographic Details
Main Authors: REN, Yazhou, HU, Kangrong, DAI, Xinyi, PAN, Lili, HOI, Steven C. H., XU, Zenglin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/4188
https://ink.library.smu.edu.sg/context/sis_research/article/5191/viewcontent/Semi_supervised_deep_embedded_afv.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-5191
record_format dspace
spelling sg-smu-ink.sis_research-51912020-04-07T05:54:45Z Semi-supervised deep embedded clustering REN, Yazhou HU, Kangrong DAI, Xinyi PAN, Lili HOI, Steven C. H. XU, Zenglin Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints in the feature learning process such that data samples belonging to the same cluster are close to each other and data samples belonging to different clusters are far away from each other in the learned feature space. Extensive experiments on real benchmark data sets validate the effectiveness and robustness of the proposed method. 2019-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4188 info:doi/10.1016/j.neucom.2018.10.016 https://ink.library.smu.edu.sg/context/sis_research/article/5191/viewcontent/Semi_supervised_deep_embedded_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Semi-supervised learning Deep embedded clustering Pairwise constraints Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Semi-supervised learning
Deep embedded clustering
Pairwise constraints
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Semi-supervised learning
Deep embedded clustering
Pairwise constraints
Databases and Information Systems
Numerical Analysis and Scientific Computing
REN, Yazhou
HU, Kangrong
DAI, Xinyi
PAN, Lili
HOI, Steven C. H.
XU, Zenglin
Semi-supervised deep embedded clustering
description Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints in the feature learning process such that data samples belonging to the same cluster are close to each other and data samples belonging to different clusters are far away from each other in the learned feature space. Extensive experiments on real benchmark data sets validate the effectiveness and robustness of the proposed method.
format text
author REN, Yazhou
HU, Kangrong
DAI, Xinyi
PAN, Lili
HOI, Steven C. H.
XU, Zenglin
author_facet REN, Yazhou
HU, Kangrong
DAI, Xinyi
PAN, Lili
HOI, Steven C. H.
XU, Zenglin
author_sort REN, Yazhou
title Semi-supervised deep embedded clustering
title_short Semi-supervised deep embedded clustering
title_full Semi-supervised deep embedded clustering
title_fullStr Semi-supervised deep embedded clustering
title_full_unstemmed Semi-supervised deep embedded clustering
title_sort semi-supervised deep embedded clustering
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4188
https://ink.library.smu.edu.sg/context/sis_research/article/5191/viewcontent/Semi_supervised_deep_embedded_afv.pdf
_version_ 1770574396831825920