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)
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.
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).