@inproceedings{2a97cb297de94f4288ab6f9f2787a3f9,
title = "Exemplar-based robust coherent biclustering",
abstract = "The biclustering, co-clustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or sub-matrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a coherence measure of the biclusters. We introduce a novel formulation of the coherent biclustering problem and use it to derive two algorithms. The first algorithm is based on loopy message passing; and the second relies on a greedy strategy yielding an algorithm that is significantly faster than the first. A distinguishing feature of these algorithms is that they identify an exemplar or a prototypical member of each bicluster. We note the interference from background elements in biclustering, and offer a means to circumvent such interference using additional regularization. Our experiments with synthetic as well as real-world datasets show that our algorithms are competitive with the current state-of-the-art algorithms for finding coherent biclusters.",
author = "Kewei Tu and Xixiu Ouyang and Dingyi Han and Yong Yu and Vasant Honavar",
year = "2011",
doi = "10.1137/1.9781611972818.76",
language = "English (US)",
isbn = "9780898719925",
series = "Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011",
publisher = "Society for Industrial and Applied Mathematics Publications",
pages = "884--895",
booktitle = "Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011",
address = "United States",
note = "11th SIAM International Conference on Data Mining, SDM 2011 ; Conference date: 28-04-2011 Through 30-04-2011",
}