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Related Experiment Videos

The Deterministic Information Bottleneck.

D J Strouse1, David J Schwab2

  • 1Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A. danieljstrouse@gmail.com.

Neural Computation
|April 15, 2017
PubMed
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The deterministic information bottleneck (DIB) offers a computationally efficient alternative to the information bottleneck (IB) method for feature selection. DIB uses entropy for compression, achieving better performance and enabling interpolation between soft and hard clustering.

Area of Science:

  • Machine Learning
  • Information Theory
  • Data Compression

Background:

  • Lossy compression and clustering rely on identifying relevant features.
  • The Information Bottleneck (IB) method formalizes this using mutual information for optimal trade-offs.
  • Existing IB methods often use stochastic encoders (soft clustering).

Purpose of the Study:

  • Introduce a novel formulation, the Deterministic Information Bottleneck (DIB), using entropy instead of mutual information.
  • Compare DIB's performance and efficiency against the traditional IB method.
  • Develop a method for continuous interpolation between soft and hard clustering.

Main Methods:

  • Formulated the Deterministic Information Bottleneck (DIB) by replacing mutual information with entropy.

Related Experiment Videos

  • Conducted comparative experiments on synthetic datasets.
  • Analyzed computational efficiency across various convergence parameters.

    • Main Results:

    • DIB and IB showed similar performance on the IB cost function.
    • DIB significantly outperformed IB on the DIB cost function.
    • DIB demonstrated considerable computational efficiency gains over IB.

    Conclusions:

    • The DIB provides a more effective compression measure using entropy.
    • DIB's deterministic nature leads to hard clustering, offering computational advantages.
    • The DIB framework facilitates a spectrum of clustering approaches from soft to hard.