Matrix Tri-Factorization Over the Tropical Semiring
Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, and decision problems. Previous studies have applied a matrix two-factorization algorithm based on the tropical semiring to investigate bipartite and tripartite netwo...
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Published in | IEEE access Vol. 11; pp. 69022 - 69032 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, and decision problems. Previous studies have applied a matrix two-factorization algorithm based on the tropical semiring to investigate bipartite and tripartite networks. Tri-factorization algorithms based on standard linear algebra are used to solve tasks such as data fusion, co-clustering, matrix completion, community detection, and more. However, there is currently no tropical matrix tri-factorization approach that would allow for the analysis of multipartite networks with many parts. To address this, we propose the triFastSTMF algorithm, which performs tri-factorization over the tropical semiring. We applied it to analyze a four-partition network structure and recover the edge lengths of the network. We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network. When trained on a specific subnetwork and used to predict the entire network, triFastSTMF outperforms Fast-NMTF by several orders of magnitude smaller error. The robustness of triFastSTMF is due to tropical operations, which are less prone to predict large values compared to standard operations. |
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AbstractList | Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, and decision problems. Previous studies have applied a matrix two-factorization algorithm based on the tropical semiring to investigate bipartite and tripartite networks. Tri-factorization algorithms based on standard linear algebra are used to solve tasks such as data fusion, co-clustering, matrix completion, community detection, and more. However, there is currently no tropical matrix tri-factorization approach that would allow for the analysis of multipartite networks with many parts. To address this, we propose the triFastSTMF algorithm, which performs tri-factorization over the tropical semiring. We applied it to analyze a four-partition network structure and recover the edge lengths of the network. We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network. When trained on a specific subnetwork and used to predict the entire network, triFastSTMF outperforms Fast-NMTF by several orders of magnitude smaller error. The robustness of triFastSTMF is due to tropical operations, which are less prone to predict large values compared to standard operations. |
Author | Omanovic, Amra Oblak, Polona Curk, Tomaz |
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References | ref13 Omanović (ref12) 2022 ref15 ref14 ref30 ref11 ref2 ref1 ref17 Hook (ref4) 2017 ref19 Zhang (ref16); 80 Wang (ref18) Mnih (ref10) ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref3 ref6 ref5 |
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SubjectTerms | Algorithms Approximation algorithms Bioinformatics Clustering Data integration Discrete event systems Factorization four-partition network Linear algebra Matrices (mathematics) network structure analysis Optimal control Optimization Partitioning algorithms Prediction algorithms Task analysis tri-factorization Tropical semiring |
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Title | Matrix Tri-Factorization Over the Tropical Semiring |
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