• DOI:10.1109/ICASSP.2017.7953382
Corpus ID: 7312060

Compressive K-means

@article{Keriven2017CompressiveK,
  title={Compressive K-means},
  author={N. Keriven and N. Tremblay and Y. Traonmilin and R. Gribonval},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={6369-6373}
}

N. Keriven, N. Tremblay, +1 author R. Gribonval

Published 2017

Computer Science, Mathematics

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compressive version of K-means (CKM), that estimates cluster centers from a sketch, i.e. from a drastically compressed representation of the training dataset. We demonstrate empirically that CKM performs similarly to Lloyd-Max, for a sketch size proportional to the number of centroids times the ambient dimension, and… CONTINUE READING

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