Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning
Publication Date
2022-05Journal Title
Data Mining and Knowledge Discovery
ISSN
1384-5810
Publisher
Springer Science and Business Media LLC
Volume
36
Issue
3
Pages
958-986
Language
en
Type
Article
This Version
VoR
Metadata
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Peng, H., & Pavlidis, N. G. (2022). Weighted Sparse Subspace Representation: A Unified Framework for
Subspace Clustering, Constrained Clustering, and Active Learning. Data Mining and Knowledge Discovery, 36 (3), 958-986. https://doi.org/10.1007/s10618-022-00820-9
Abstract
Spectral-based subspace clustering methods have proved successful in many
challenging applications such as gene sequencing, image recognition, and motion
segmentation. In this work, we first propose a novel spectral-based subspace
clustering algorithm that seeks to represent each point as a sparse convex
combination of a few nearby points. We then extend the algorithm to constrained
clustering and active learning settings. Our motivation for developing such a
framework stems from the fact that typically either a small amount of labelled
data is available in advance; or it is possible to label some points at a cost.
The latter scenario is typically encountered in the process of validating a
cluster assignment. Extensive experiments on simulated and real data sets show
that the proposed approach is effective and competitive with state-of-the-art
methods.
Keywords
Article, Special Issue of the Journal Track of ECML PKDD 2022, Subspace clustering, Constrained clustering, Active learning
Identifiers
s10618-022-00820-9, 820
External DOI: https://doi.org/10.1007/s10618-022-00820-9
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337193
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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