A sparse exponential family latent block model for co-clustering

Saeid Hoseinipour (), Mina Aminghafari (), Adel Mohammadpour () and Mohamed Nadif ()
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Saeid Hoseinipour: Amirkabir University of Technology (Tehran Polytechnic), Department of Mathematics and Computer Science
Mina Aminghafari: University of Calgary, Department of Mathematics and Statistics
Adel Mohammadpour: Amirkabir University of Technology (Tehran Polytechnic), Department of Mathematics and Computer Science
Mohamed Nadif: Université Paris Cité, Centre Borelli UMR 9010

Advances in Data Analysis and Classification, 2025, vol. 19, issue 4, No 5, 987 pages

Abstract: Abstract Over the last decades, co-clustering models have spawned a number of algorithms showing the advantages that co-clustering can have over clustering. This is especially true for sparse high-dimensional data such as document-word matrices, which are our focus here. This proposal uses Latent Block Models (LBMs), rigorous statistical models that offer a variety of benefits in terms of flexibility, parsimony, and effectiveness. LBMs have been proposed in relation to different data types. This paper aims to embed existing and new models in a unified framework, focusing on exponential family LBM (ELBM) and the classification maximum likelihood approach. We then extend these models to include sparse versions, known as SELBM, taking into account the sparsity of datasets. The matrix formulations that we propose lead to simplified algorithms capable of addressing various types of data effectively.

Keywords: Co-clustering; Exponential family; Latent block model; Classification maximum likelihood; Sparsity; Text mining; 62H30; 68T10; 91C20; 62E15; 62E10 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-024-00608-3

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