Corpus ID: 7268328

Unsupervised Learning by Convex and Conic Coding

@inproceedings{Lee1996UnsupervisedLB,
  title={Unsupervised Learning by Convex and Conic Coding},
  author={Daniel D. Lee and H. Sebastian Seung},
  booktitle={NIPS},
  year={1996}
}
  • Daniel D. Lee, H. Sebastian Seung
  • Published in NIPS 1996
  • Computer Science
  • Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic combination of basis vectors to the input. The learning algorithms produce basis vectors that minimize the reconstruction error of the encoders. The convex algorithm develops locally linear models of the input, while the conic algorithm discovers features. Both algorithms are used to model handwritten digits and compared with vector quantization and principal component… CONTINUE READING

    Figures and Topics from this paper.

    Explore key concepts

    Links to highly relevant papers for key concepts in this paper:

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 112 CITATIONS

    Learning in Intelligent Embedded Systems

    2 Semi-NMF and Convex-NMF

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    Archetypal analysis for machine learning and data mining

    VIEW 4 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    FILTER CITATIONS BY YEAR

    1997
    2020

    CITATION STATISTICS

    • 6 Highly Influenced Citations

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 16 REFERENCES