Melvin's digital garden


CREATED: 201001261255 LINK: url:~/Modules/Literature/Brown2010a.pdf Title: Decoding HMMs using the k best paths: algorithms and applications

HMM gives probability distribution over path/sequences, originally used in speech recognition

annotion = interval of states

Viterbi vs posterior decoding

  • $O(nm)$ memory is prohibitive
  • Viterbi is a single explanation, may have very small prob
  • Posterior is a summary over all paths but may not represent a legal path

Idea: compute k best paths and summarize them DP algorithm has $O(knm^2)$ time and $O(knm)$ memory, memory can be reduced by active pruning about $km + \lg n$ space in practice

Summarize using weighted average of the boundaries

Good for ambiguous sequences with several valid alternate explanations

Clustering of k-best paths?

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