Manifold preserving hierarchical topic models for quantization and approximation

Minje Kim, Paris Smaragdis

Research output: Contribution to conferencePaper

Abstract

We present two complementary topic models to address the analysis of mixture data lying on manifolds. First, we propose a quantization method with an additional mid-layer latent variable, which selects only data points that best preserve the manifold structure of the input data. In order to address the case of modeling all the in-between parts of that manifold using this reduced representation of the input, we introduce a new model that provides a manifold-aware interpolation method. We demonstrate the advantages of these models with experiments on the hand-written digit recognition and the speech source separation tasks.

Original languageEnglish (US)
Pages2410-2418
Number of pages9
StatePublished - Jan 1 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: Jun 16 2013Jun 21 2013

Other

Other30th International Conference on Machine Learning, ICML 2013
CountryUnited States
CityAtlanta, GA
Period6/16/136/21/13

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Sociology and Political Science

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    Kim, M., & Smaragdis, P. (2013). Manifold preserving hierarchical topic models for quantization and approximation. 2410-2418. Paper presented at 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, United States.