Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling

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 multiway cut 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.

Authors

• Daisuke Hatano RIKEN AIP
• Takuro Fukunaga Chuo University
• Takanori Maehara
• Ken-ichi Kawarabayashi National Institute of Informatics The University of Tokyo

Keywords

No keywords are indexed for this paper.