Automatically weighted binary multi-view clustering via deep initialization (AW-BMVC)

Autor: Khamis Houfar, Djamel Samai, Fadi Dornaika, Azeddine Benlamoudi, Khaled Bensid, Abdelmalik Taleb-Ahmed
Přispěvatelé: Université Kasdi Merbah Ouargla, Universitat Autònoma de Barcelona (UAB), Ikerbasque - Basque Foundation for Science, University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Center for Machine Vision Research (CMV), University of Oulu, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université Polytechnique Hauts-de-France (UPHF), The authors gratefully acknowledge the Directorate General for Scientific Research and Technological Development (DGRSDT) of Algeria for the financial support to this work. This work is part of the grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe., Universitat Autònoma de Barcelona [UAB], University of the Basque Country/Euskal Herriko Unibertsitatea [UPV/EHU], Center for Machine Vision Research [CMV], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN], COMmunications NUMériques - IEMN [COMNUM - IEMN], Université Polytechnique Hauts-de-France [UPHF]
Rok vydání: 2023
Předmět:
Zdroj: Pattern Recognition
Pattern Recognition, 2023, 137, pp.109281. ⟨10.1016/j.patcog.2022.109281⟩
ISSN: 0031-3203
Popis: International audience; Clustering is inherently a process of exploratory data analysis. It has attracted more attention recently because much real-world data consists of multiple representations or views. However, it becomes increasingly problematic when dealing with large and heterogeneous data. It is worth noting that several approaches have been developed to increase computational efficiency, although most of them have some drawbacks: (1) Most existing techniques consider equal or static weights to quantify importance across different views and samples, so common and complementary features cannot be used. (2) The clustering task is performed by arbitrary initialization without caring about the rich structure of the joint discrete representation, and thus poorly executed. In this paper, we propose a novel approach called “Auto-Weighted Binary Multi-View Clustering Via Deep Initialization” for large-scale multi-view clustering based on two main scenarios. First, we consider the distinction between different views based on the importance of samples, and therefore apply a dynamic learning strategy for the automatic weighting of views and samples. Second, in the context of initializing binary clustering, we develop a new CNN feature and use a low-dimensional binary embedding by exploiting the efficient capabilities of Fourier mapping. Moreover, our approach simultaneously learns a joint discrete representation and performs direct clustering using a constrained binary matrix factorization; the optimization problem is perfectly solved in a unified learning model. Experimental results conducted on several challenging datasets demonstrate the effectiveness and superiority of the proposed approach over state-of-the-art methods in terms of accuracy, normalized mutual information, and purity.
Databáze: OpenAIRE
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