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Generating Strictly Controlled Stimuli for Figure Recognition Experiments
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Published on: March 18, 2019

Learning the parts of objects by non-negative matrix factorization.

D D Lee1, H S Seung

  • 1Bell Laboratories, Lucent Technologies, Murray Hill, New Jersey 07974, USA.

Nature
|November 5, 1999
PubMed
Summary

This study introduces a novel algorithm, non-negative matrix factorization (NMF), that learns object parts for better recognition. Unlike other methods, NMF uses constraints to enable additive combinations, revealing parts-based representations.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Cognitive psychology

Background:

  • Psychological and physiological evidence supports parts-based representations in the brain.
  • Computational theories of object recognition often utilize parts-based representations.
  • The mechanism by which brains or computers learn object parts remains an open question.

Purpose of the Study:

  • To demonstrate an algorithm capable of learning parts of objects.
  • To contrast this approach with methods that learn holistic representations.
  • To investigate the role of non-negativity constraints in representation learning.

Main Methods:

  • Developed a non-negative matrix factorization (NMF) algorithm.
  • Applied NMF to learn parts of faces and semantic features of text.
  • Compared NMF with principal components analysis (PCA) and vector quantization (VQ).

Main Results:

  • NMF successfully learned parts-based representations for faces and text.
  • NMF contrasted with PCA and VQ, which produced holistic representations.
  • Non-negativity constraints in NMF enabled additive, parts-based combinations.

Conclusions:

  • Non-negative matrix factorization provides a method for learning parts-based representations.
  • The non-negativity constraints are key to achieving parts-based representations.
  • Implementing NMF as a neural network with non-negative firing rates and synaptic strengths naturally yields parts-based representations.