Abstract: We discuss several approaches to similarity preserving coding of symbol sequences and possible connections of their distributed versions to metric embeddings. Interpreting sequence representation methods with embeddings can help develop an approach to their analysis and may lead to discovering useful properties.

Keywords: sequence similarity, metric embeddings, distributed representations, neural networks

ACM Classification Keywords: I.2.6 Connectionism and neural nets, E.m Miscellaneous, G.2.3 Applications

Link:

APPROACHES TO SEQUENCE SIMILARITY REPRESENTATION

Artem Sokolov, Dmitri Rachkovskij

http://www.foibg.com/ijita/vol13/ijita13-3-p11.pdf