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Multi-view Spectral Clustering via Integrating Label and Data Graph Learning

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Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13233))

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Abstract

Nowadays, one-step multi-view clustering algorithms attract many interests. The main issue of multi-view clustering approaches is how to combine the information extracted from the available views. A popular approach is to use view-based graphs and/or a consensus graph to describe the different views. We introduce a novel one-step graph-based multi-view clustering approach in this study. Our suggested method, in contrast to existing graph-based one-step clustering methods, provides two major novelties to the method called Nonnegative Embedding and Spectral Embedding (NESE) proposed in the recent paper [1]. To begin, we use the cluster label correlation to create an additional graph in addition to the graphs associated with the data space. Second, the cluster-label matrix is constrained by adopting some restrictions to make it more consistent. The effectiveness of the proposed method is demonstrated by experimental results on many public datasets.

Supported by F.R.S-FNRS.

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Notes

  1. 1.

    https://cam-orl.co.uk/facedatabase.html.

  2. 2.

    https://scholar.googleusercontent.com/scholar?q=cache:Dxo2Hbfln2sJ:scholar.google.com/hl=enas-sdt=0,5.

  3. 3.

    https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html.

  4. 4.

    https://www.researchgate.net/publication/335857675.

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Acknowledgment

This research was funded by the INTER program, co-funded by the FNR (Fond National de la Recherche, Luxembourg) and the Fund for Scientific Research-FNRS, Belgium (F.R.S-FNRS), grant number 19-14016367 - ‘Sustainable Residential Densification’ project (SusDens, 2020–2023).

Author information

Authors and Affiliations

  1. University of the Basque Country UPV/EHU, San Sebastian, Spain

    Sally El Hajjar & Fadi Dornaika

  2. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Fadi Dornaika

  3. Lebanese University, Beirut, Lebanon

    Fahed Abdallah

  4. Urban Development and Mobility Department, Luxembourg Institute of Socio-Economic Research (LISER), 11 Porte des Sciences, 4366, Esch-sur-Alzette, Luxembourg

    Fahed Abdallah & Hichem Omrani

Authors
  1. Sally El Hajjar
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  2. Fadi Dornaika
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  3. Fahed Abdallah
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  4. Hichem Omrani
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Corresponding author

Correspondence to Fadi Dornaika .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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El Hajjar, S., Dornaika, F., Abdallah, F., Omrani, H. (2022). Multi-view Spectral Clustering via Integrating Label and Data Graph Learning. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13233. Springer, Cham. https://doi.org/10.1007/978-3-031-06433-3_10

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  • DOI: https://doi.org/10.1007/978-3-031-06433-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06432-6

  • Online ISBN: 978-3-031-06433-3

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