Sequential Information Bottleneck for Finite Data

Jaakko Peltonen, Janne Sinkkonen, Samuel Kaski

ICML 2004

doi:10.1145/1015330.1015375 /icml/2004/peltonen2004icml-sequential/

Abstract

The sequential information bottleneck (sIB) algorithm clusters co-occurrence data such as text documents vs. words. We introduce a variant that models sparse co-occurrence data by a generative process. This turns the objective function of sIB, mutual information, into a Bayes factor, while keeping it intact asymptotically, for non-sparse data. Experimental performance of the new algorithm is comparable to the original sIB for large data sets, and better for smaller, sparse sets.

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Cite

Text

Peltonen et al. "Sequential Information Bottleneck for Finite Data." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015375

Markdown

[Peltonen et al. "Sequential Information Bottleneck for Finite Data." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/peltonen2004icml-sequential/) doi:10.1145/1015330.1015375

BibTeX

@inproceedings{peltonen2004icml-sequential,
  title     = {{Sequential Information Bottleneck for Finite Data}},
  author    = {Peltonen, Jaakko and Sinkkonen, Janne and Kaski, Samuel},
  booktitle = {International Conference on Machine Learning},
  year      = {2004},
  doi       = {10.1145/1015330.1015375},
  url       = {https://mlanthology.org/icml/2004/peltonen2004icml-sequential/}
}