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Probabilistic Dimensionality Reduction via Structure Learning
[article]
2016
arXiv
pre-print
We propose a novel probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework, we present a new model, which is able to learn a smooth skeleton of embedding points in a low-dimensional space from high-dimensional noisy data. The formulation of the new model can be equivalently interpreted as two coupled learning problem, i.e., structure learning and the learning of projection matrix. This
arXiv:1610.04929v1
fatcat:vfa2vzayqve3fltm6srmytzrvy