Multi-view biclustering via non-negative matrix tri-factorisation
File(s)
Published version
Author(s)
Orme, Ella SC
Rodosthenous, Theodoulos
Evangelou, Marina
Type
Journal Article
Abstract
Multi-view data is ever more apparent as methods for production, collection and storage of data become more feasible both practically and fiscally. However, not all features are relevant to describe the patterns for all individuals. Multi-view biclustering aims to simultaneously cluster both rows and columns, discovering clusters of rows as well as their view-specific identifying features. A novel multi-view biclustering approach based on non-negative matrix factorisation is proposed named ResNMTF. Demonstrated through extensive experiments on both synthetic and real datasets, ResNMTF successfully identifies both overlapping and non-exhaustive biclusters, without pre-existing knowledge of the number of biclusters present, and is able to incorporate any combination of shared dimensions across views. Further, to address the lack of a suitable bicluster-specific intrinsic measure, the popular silhouette score is extended to the bisilhouette score. The bisilhouette score is demonstrated to align well with known extrinsic measures, and proves useful as a tool for hyperparameter tuning as well as visualisation.
Date Issued
2026-04-01
Date Acceptance
2025-09-17
Citation
Pattern Recognition, 2026, 172 (Part B)
ISSN
0031-3203
Publisher
Elsevier BV
Journal / Book Title
Pattern Recognition
Volume
172
Issue
Part B
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
112454
Date Publish Online
2025-09-20