Hidden Markov Model by Matrix and Tensor Decomposition
Overview
Solves Hidden Markov Models (HMMs) via matrix and tensor decomposition. Converts observation sequences to co-occurrence matrices/tensors and applies Symmetric Non-negative Matrix Factorization (symNMF), Singular Value Decomposition (SVD), CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT) decomposition to recover HMM parameters. Also provides standard HMM algorithms (Forward, Backward, Viterbi, Baum-Welch) for comparison. The spectral learning approach for HMMs is based on Hsu, Kakade, and Zhang (2012) doi:10.1016/j.jcss.2011.12.025. The symNMF method is described in Kuang, Yun, and Park (2015) doi:10.1007/s10898-014-0247-2. The Tensor-Train decomposition is described in Oseledets (2011) doi:10.1137/090752286.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK7 OK2026-05-28
Documentation
- Help pages
- 7
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 43%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Code & Tests
People & History
- 0.1.0Latest2026-05-27 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-05-27
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 10 KB
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