Pruning subsequence search with attention-based embedding

@article{Raffel2016PruningSS,
  title={Pruning subsequence search with attention-based embedding},
  author={Colin Raffel and Daniel P. W. Ellis},
  journal={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2016},
  pages={554-558}
}
Searching a large database to find a sequence that is most similar to a query can be prohibitively expensive, particularly if individual sequence comparisons involve complex operations such as warping. To achieve scalability, "pruning" heuristics are typically employed to minimize the portion of the database that must be searched with more complex matching. We present an approximate pruning technique which involves embedding sequences in a Euclidean space. Sequences are embedded using a… CONTINUE READING

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