This set of algorithms includes three multi-view bi-clustering methods. All three methods can be used to identify clusters from multi-view data that are with consensus from all views and simultaneously identify features from each view that are associated with the identified clusters. The implementation of all these methods are in both Matlab ard R.
A Cross-species Bi-clustering Approach to Identifying Conserved Co-regulated Genes
Jiangwen Sun, Zongliang Jiang, Xiuchun Tian and Jinbo Bi
Bioinformatics, 32 (12), i137-i146, 2016
Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization
Jiangwen Sun, Jin Lu, Tiangyang Xu and Jinbo Bi
In the Proceedings of The 32nd International Conference on Machine Learning (ICML), 2015.
Multi-view Singular Value Decomposition for Disease Subtyping and Genetic Associations
Jiangwen Sun, Jinbo Bi and Henry R Kranzler
BMC Genetics, 15 (1), 2014