SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Diogo Pernes (Author) FEUP; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: Proceedings of the International Joint Conference on Neural Networks

Pages: 1-10

2019 International Joint Conference on Neural Networks, IJCNN 2019

14 July 2019 through 19 July 2019

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00R-57Y

DOI: 10.1109/ijcnn.2019.8851929

Abstract (EN): We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10