Learning Sparse Hypergraphs from Dyadic Relational Data

Coutino, M and Chepuri, SP and Leus, G (2019) Learning Sparse Hypergraphs from Dyadic Relational Data. In: 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 15-18 December 2019, pp. 216-220.

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Abstract

In this paper, we propose a structured hypergraph learning algorithm based on the structure of common statistical dependencies observed in network datasets. By considering the social relations regression model (SRRM) as starting point, we extend the explanatory power of the model by including third-order dependency patterns through a hypergraph which exhibit the multi-clustered behavior of the network data. To unveil the underlying structure of the data, the hypergraph learning problem is considered as a combination of a generalized factor analysis problem, regularized by a smoothness assumption over the network data feature space, and a dictionary learning problem, which can be shown to be solved efficiently. Experimental results in both synthetic and real datasets illustrate the performance of the proposed hypergraph learning method. © 2019 IEEE.

Item Type:	Conference Paper
Publication:	2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright of this article belongs to IEEE
Keywords:	Array processing; Factor analysis; Graph theory; Learning algorithms; Multivariant analysis; Regression analysis, Analysis problems; Dictionary learning; Explanatory power; Hypergraph; Learning methods; Network Clustering; Regression model; Statistical dependencies, Learning systems
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	14 Jul 2021 11:25
Last Modified:	14 Jul 2021 11:25
URI:	http://eprints.iisc.ac.in/id/eprint/65083

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