Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling
Abstract
We propose a scalable and efficient algorithm for coclustering a higher-order tensor. Viewing tensors with hypergraphs, we propose formulating the co-clustering of a tensor as a problem of partitioning the corresponding hypergraph. Our algorithm is based on the random sampling technique, which has been successfully applied to graph cut problems. We extend a random sampling algorithm for the graph multiwaycut problem to hypergraphs, and design a co-clustering algorithm based on it. Each iteration of our algorithm runs in polynomial on the size of hypergraphs, and thus it performs well even for higher-order tensors, which are difficult to deal with for state-of-the-art algorithm.
Cite
Text
Hatano et al. "Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10914Markdown
[Hatano et al. "Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/hatano2017aaai-scalable/) doi:10.1609/AAAI.V31I1.10914BibTeX
@inproceedings{hatano2017aaai-scalable,
title = {{Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling}},
author = {Hatano, Daisuke and Fukunaga, Takuro and Maehara, Takanori and Kawarabayashi, Ken-ichi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {1992-1999},
doi = {10.1609/AAAI.V31I1.10914},
url = {https://mlanthology.org/aaai/2017/hatano2017aaai-scalable/}
}