Search Machine Learning Repository: Manifold Preserving Hierarchical Topic Models for Quantization and Approximation
Authors: Minje Kim and Paris Smaragdis
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Year: 2013
Pages: 1373-1381
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.
[pdf] [BibTeX]

authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).