Weighted sparse simplex representation: a unified framework for subspace clustering, constrained clustering, and active learning
| cam.issuedOnline | 2022-02-11 | |
| dc.contributor.author | Peng, Hankui | |
| dc.contributor.author | Pavlidis, Nicos G | |
| dc.contributor.orcid | Peng, Hankui [0000-0003-1623-9852] | |
| dc.date.accessioned | 2022-05-16T16:00:48Z | |
| dc.date.available | 2022-05-16T16:00:48Z | |
| dc.date.issued | 2022-05 | |
| dc.date.submitted | 2021-03-07 | |
| dc.date.updated | 2022-05-16T16:00:47Z | |
| dc.description.abstract | <jats:title>Abstract</jats:title><jats:p>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 a constrained clustering and active learning framework. Our motivation for developing such a framework stems from the fact that typically either a small amount of labelled data are 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 datasets show that the proposed approach is effective and competitive with state-of-the-art methods.</jats:p> | |
| dc.identifier.doi | 10.17863/CAM.84611 | |
| dc.identifier.eissn | 1573-756X | |
| dc.identifier.issn | 1384-5810 | |
| dc.identifier.other | s10618-022-00820-9 | |
| dc.identifier.other | 820 | |
| dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/337193 | |
| dc.language | en | |
| dc.language.iso | eng | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.publisher.url | http://dx.doi.org/10.1007/s10618-022-00820-9 | |
| dc.subject | cs.LG | |
| dc.subject | stat.ML | |
| dc.subject | stat.ML | |
| dc.title | Weighted sparse simplex representation: a unified framework for subspace clustering, constrained clustering, and active learning | |
| dc.type | Article | |
| dcterms.dateAccepted | 2022-01-13 | |
| prism.endingPage | 986 | |
| prism.issueIdentifier | 3 | |
| prism.publicationName | Data Mining and Knowledge Discovery | |
| prism.startingPage | 958 | |
| prism.volume | 36 | |
| rioxxterms.licenseref.uri | http://creativecommons.org/licenses/by/4.0/ | |
| rioxxterms.version | VoR | |
| rioxxterms.versionofrecord | 10.1007/s10618-022-00820-9 |
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