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Communication Dans Un Congrès Année : 2022

Parallel Spectral Clustering with FEAST Library

Résumé

Spectral clustering is one of the most relevant unsupervised learning methods capable of classifying data without any a priori information. At the heart of this method is the computation of the dominant eigenvectors of an affinity matrix in order to work on a low-dimensional data space in which the clustering is made. We propose in this paper a study of the integration of the FEAST library to compute these eigenvectors in our parallel spectral clustering method by domain decomposition. We also show that this library allows to add a second level of parallelism in addition to the domain decomposition level.
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Dates et versions

hal-04296302 , version 1 (20-11-2023)

Identifiants

Citer

Saad Mdaa, Anass Ouali Alami, Ronan Guivarch, Sandrine Mouysset. Parallel Spectral Clustering with FEAST Library. 2nd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2022), Sep 2022, Santiago de Compostela, Spain. pp.127-138, ⟨10.1007/978-3-031-20319-0_10⟩. ⟨hal-04296302⟩
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