EM algorithm for GTM-FS

Dharmesh M. Maniyar, Ian T. Nabney

Research output: Working paperTechnical report

Abstract

We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.
Original languageEnglish
Place of PublicationBirmingham
PublisherAston University
Number of pages5
ISBN (Print)NCRG/2005/012
Publication statusPublished - 2 Nov 2005

Fingerprint

Data visualization
Feature extraction
Visualization

Keywords

  • generative topographic mapping
  • data visualization
  • simultaneous feature selection
  • Expectation-Maximization algorithm
  • GTM-FS

Cite this

Maniyar, D. M., & Nabney, I. T. (2005). EM algorithm for GTM-FS. Birmingham: Aston University.
Maniyar, Dharmesh M. ; Nabney, Ian T. / EM algorithm for GTM-FS. Birmingham : Aston University, 2005.
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Maniyar, DM & Nabney, IT 2005 'EM algorithm for GTM-FS' Aston University, Birmingham.

EM algorithm for GTM-FS. / Maniyar, Dharmesh M.; Nabney, Ian T.

Birmingham : Aston University, 2005.

Research output: Working paperTechnical report

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AU - Maniyar, Dharmesh M.

AU - Nabney, Ian T.

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AB - We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.

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Maniyar DM, Nabney IT. EM algorithm for GTM-FS. Birmingham: Aston University. 2005 Nov 2.