DC Field | Value | Language |
---|---|---|
dc.contributor.author | Doo, Woojin | ko |
dc.contributor.author | Kim, Heeyoung | ko |
dc.date.accessioned | 2024-09-05T11:00:12Z | - |
dc.date.available | 2024-09-05T11:00:12Z | - |
dc.date.created | 2024-08-29 | - |
dc.date.issued | 2024-06 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.36, no.6, pp.2629 - 2642 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322722 | - |
dc.description.abstract | Existing deep learning methods for clustering high-dimensional data perform feature selection and clustering separately, which can result in the exclusion of some important features for clustering. In this paper, we propose a method that performs deep clustering and feature selection simultaneously by inserting a concrete selector layer between the input layer and the first encoder layer of a modified autoencoder. The concrete selector layer performs feature selection, while the modified autoencoder performs clustering in the latent space by incorporating K-means loss and inter-cluster distances. The proposed method, called the K-concrete autoencoder, selects features important for clustering and uses only the selected features to learn K-means-friendly latent representations of the data. Moreover, we propose an extension of the K-concrete autoencoder to provide relative importance of each selected feature. We demonstrate the effectiveness of the proposed method using simulated and real datasets. | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Simultaneous Deep Clustering and Feature Selection via K-Concrete Autoencoder | - |
dc.type | Article | - |
dc.identifier.wosid | 001245459400013 | - |
dc.identifier.scopusid | 2-s2.0-85174844240 | - |
dc.type.rims | ART | - |
dc.citation.volume | 36 | - |
dc.citation.issue | 6 | - |
dc.citation.beginningpage | 2629 | - |
dc.citation.endingpage | 2642 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.identifier.doi | 10.1109/TKDE.2023.3323580 | - |
dc.contributor.localauthor | Kim, Heeyoung | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Autoencoder | - |
dc.subject.keywordAuthor | deep clustering | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | high-dimensional data | - |
dc.subject.keywordAuthor | K-means | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | NETWORK | - |
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