Taştan, A. ; Muma, M. ; Zoubir, A. M. (2021)
Robust Spectral Clustering: A Locality Preserving Feature Mapping Based on M-estimation.
29th European Signal Processing Conference (EUSIPCO). virtual Conference (23.-27.08.2021)
doi: 10.23919/EUSIPCO54536.2021.9616292
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Dimension reduction is a fundamental task in spectral clustering. In practical applications, the data may be corrupted by outliers and noise, which can obscure the underlying data structure. The effect is that the embeddings no longer represent the true cluster structure. We therefore propose a new robust spectral clustering algorithm that maps each high-dimensional feature vector onto a low-dimensional vector space. Robustness is achieved by posing the locality preserving feature mapping problem in form of a ridge regression task that is solved with a penalized M-estimation approach. An unsupervised penalty parameter selection strategy is proposed using the Fiedler vector, which is the eigenvector associated with the second smallest eigenvalue of a connected graph. More precisely, the penalty parameter is selected, such that, the corresponding Fiedler vector is Δ-separated with a minimum information loss on the embeddings. The method is benchmarked against popular embedding and spectral clustering approaches using real-world datasets that are corrupted by outliers.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Taştan, A. ; Muma, M. ; Zoubir, A. M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Robust Spectral Clustering: A Locality Preserving Feature Mapping Based on M-estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 8 Dezember 2021 |
| Verlag: | IEEE |
| Buchtitel: | 29th European Signal Processing Conference (EUSIPCO 2021): Proceedings |
| Veranstaltungstitel: | 29th European Signal Processing Conference (EUSIPCO) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 23.-27.08.2021 |
| DOI: | 10.23919/EUSIPCO54536.2021.9616292 |
| Kurzbeschreibung (Abstract): | Dimension reduction is a fundamental task in spectral clustering. In practical applications, the data may be corrupted by outliers and noise, which can obscure the underlying data structure. The effect is that the embeddings no longer represent the true cluster structure. We therefore propose a new robust spectral clustering algorithm that maps each high-dimensional feature vector onto a low-dimensional vector space. Robustness is achieved by posing the locality preserving feature mapping problem in form of a ridge regression task that is solved with a penalized M-estimation approach. An unsupervised penalty parameter selection strategy is proposed using the Fiedler vector, which is the eigenvector associated with the second smallest eigenvalue of a connected graph. More precisely, the penalty parameter is selected, such that, the corresponding Fiedler vector is Δ-separated with a minimum information loss on the embeddings. The method is benchmarked against popular embedding and spectral clustering approaches using real-world datasets that are corrupted by outliers. |
| Freie Schlagworte: | emergenCITY_CPS |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
| TU-Projekte: | HMWK|III L6-519/03/05.001-(0016)|emergenCity TP Bock |
| Hinterlegungsdatum: | 15 Sep 2021 13:34 |
| Letzte Änderung: | 02 Feb 2023 09:43 |
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