Parametric embedding for class visualization

Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

In this paper, we propose a new method, Parametric Embedding (PE), for visualizing the posteriors estimated over a mixture model. PE simultaneously embeds both objects and their classes in a low-dimensional space. PE takes as input a set of class posterior vectors for given data points, and tries to preserve the posterior structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a Gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semi-supervised and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of web pages, semi-supervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, Latent Dirichlet Allocation.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PublisherNeural information processing systems foundation
ISBN (Print)0262195348, 9780262195348
StatePublished - Jan 1 2005
Externally publishedYes
Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
Duration: Dec 13 2004Dec 16 2004

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other18th Annual Conference on Neural Information Processing Systems, NIPS 2004
CountryCanada
CityVancouver, BC
Period12/13/0412/16/04

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., & Tenenbaum, J. B. (2005). Parametric embedding for class visualization. In Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004 (Advances in Neural Information Processing Systems). Neural information processing systems foundation.