Speeding Up HMM Decoding and Training by Exploiting Sequence Repetitions

CPM 2007 best paper award

This page contains links to the conference and journal versions of this paper, as well as the conference presentation and a couple of implementations of some of the ideas presented in the paper. The code may be download and used for scholarly and academic purposes, in which case we request that you include a reference to our paper.

  • Speeding Up HMM Decoding and Training by Exploiting Sequence Repetitions. Shay Mozes, Oren Weimann and Michal Ziv-Ukelson. In Proceedings of the 18th annual symposium on Combinatorial Pattern Matching (CPM 2007), pages 4-15.  pdf version
  • Speeding Up HMM Decoding and Training by Exploiting Sequence Repetitions. Yury Lifshits , Shay Mozes, Oren Weimann and Michal Ziv-Ukelson. In Algorithmica (2007).  pdf version

  • ppt presentation PPT presentation (CPM 2007)


    We present a method to speed up the dynamic program algorithms used for solving the HMM decoding and training problems for discrete time-independent HMMs. We discuss the application of our method to Viterbi's decoding and training algorithms, as well as to the forward-backward and Baum- Welch algorithms. Our approach is based on identifying repeated substrings in the observed input sequence. Initially, we show how to exploit repetitions of all sufficiently small substrings (this is similar to the Four Russians method). Then, we describe four algorithms based alternatively on run length encoding (RLE), Lempel-Ziv (LZ78) parsing, grammar-based compression (SLP), and byte pair encoding (BPE). Compared to Viterbi's algorithm, we achieve speedups of Theta(log n) using the Four Russians method, Omega(r/log r) using RLE, Omega(log n / k) using LZ78, Omega(r/k) using SLP, and Omega(r) using BPE, where k is the number of hidden states, n is the length of the observed sequence and r is its compression ratio (under each compression scheme). Our experimental results demonstrate that our new algorithms are indeed faster in practice. We also discuss a parallel implementation of our algorithms.


    C++ Implementations of different variants of Viterbi's algorithm

    The different C++ implementations were used for producing the results section of the paper. This is not a complete implementation of Viterbi's algorithm. All variants assume DNA sequences (alphabet size 4) and only compute the probability of the most probable sequence of hidden states. Traceback of the optimal sequence itself is not implemneted, but should be easy to add. The code is not well documented, but is not very complicated either. I will be happy to try and answer questions that arise.
  • viterbi.cpp - classical Viterbi's algorithm
  • vit_rep.cpp - Viterbi's algorithm using matrices. divides the sequence into words of size b and precomputes all matrices corresponding to all possible words.
  • trie.cpp , trie_sep.cpp , trie_dfs.cpp - Different variants that compute the LZ78 trie and run Viterbi's algorithm on the compressed sequence (see section 4.3 of the journal version). The difference between the variants is in the way the matrices are computed.
  • lz_cut.cpp , lz_cut2.cpp - Does LZ78 compression, but only uses words that appear enough times (see section 4.4 of the journal version). The two variants differ in the way they parse the input sequence into just "good" words.
  • Multithreaded Java Implementation

    I coded this as a final project for a multiprocessor synchronization class. In the project I implemented many variants of the "Four Russians" version of the algorithm. Here I did implement the traceback step for recovering the optimal sequence of hidden states. The variants differ in the ways and extent to which parallelization is employed. See the project report for details. One of these variants is a reasonable sequential implementation in Java. It is not as efficient as the DNA-special purpose C++ code, but not too bad either. The source code can be found here .