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Structured sparse K -means clustering via Laplacian smoothing

Abstract:

We propose a structured sparse K-means clustering algorithm that learns the cluster assignments and feature weights simultaneously. Compared to previous approaches, including K-means in MacQueen [28] and sparse K-means in Witten and Tibshirani [46], our method exploits the correlation information among features via the Laplacian smoothing technique, so as to achieve superior clustering accuracy. At the same time, the relevant features learned by our method are more structured, hence have bett...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1016/j.patrec.2018.06.006

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Oxford college:
Linacre
Grünewald, S More by this author
Publisher:
Elsevier Publisher's website
Journal:
Pattern Recognition Letters Journal website
Volume:
112
Pages:
63-69
Publication date:
2018-06-05
Acceptance date:
2018-06-04
DOI:
EISSN:
1872-7344
ISSN:
0167-8655
Pubs id:
pubs:929559
URN:
uri:b54c41c0-3e5e-4a04-86ab-0adc21792811
UUID:
uuid:b54c41c0-3e5e-4a04-86ab-0adc21792811
Local pid:
pubs:929559

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