Latent semantic mapping: dimensionality reduction via globally optimal continuous parameter modeling

@article{Bellegarda2005LatentSM,
  title={Latent semantic mapping: dimensionality reduction via globally optimal continuous parameter modeling},
  author={J. Bellegarda},
  journal={IEEE Workshop on Automatic Speech Recognition and Understanding, 2005.},
  year={2005},
  pages={127-132}
}
  • J. Bellegarda
  • Published 2005 in
    IEEE Workshop on Automatic Speech Recognition and…
Originally formulated in the context of information retrieval, latent semantic analysis exhibits three main characteristics: (i) discrete entities (namely words and documents) are mapped onto a continuous vector space; (ii) this mapping is determined by global correlation patterns; and (iii) dimensionality reduction is an integral part of the process. Such fairly generic properties may be advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying… CONTINUE READING
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