oMHMM (Orthogonal Mixture of Hidden Markov Models)
This repository is the source code of the following paper (cite the paper when using it):
N. Safinianaini and C. P. E. de Souza and H. Boström and J. Lagergren, "Orthogonal Mixture of Hidden Markov Models" 2020 ECML PKDD (https://ecmlpkdd2020.net/programme/accepted/)
The implementation is based on the standard EM for MHMM implementation from this paper (we disabled the sparsity feature):
Spamhmm: Sparse mixture of hidden markov models for graph connected entities. 2019 International Joint Conference on Neural Networks(IJCNN) pp. 1–10 (2019)
Datasets
- digits: “pen-based recognition of hand- written digits” dataset in the UCI machine learning repository.
- biology: from the NCBI Sequence Read Archive (SRA) under accession number SRP074289; for pre-processing see readme in directory tests/biology.
- movements: Libras movement dataset from the UCI machine learning repository.
Required Softwares
Python 3.6.2
hmmlearn 0.2.1
cvxpy 1.0.21
numpy 1.16.2
scikit-learn 0.19.1
scipy 1.1.0
Note
Due to the double-blinded review of ECML, this code was earlier created by myself, however, anonymously under the contributer name ecml20200330.