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Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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
doi:10.1609/aaai.v31i1.10914
fatcat:33s6w3gimbdsfoml66it2kve4y