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» Semi-Supervised Dimensionality Reduction
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BIOWIRE
2007
Springer
15 years 10 months ago
Beta Random Projection
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. In particular, r...
Yu-En Lu, Pietro Liò, Steven Hand
135
Voted
JMLR
2010
155views more  JMLR 2010»
14 years 10 months ago
Bayesian Gaussian Process Latent Variable Model
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
Michalis Titsias, Neil D. Lawrence
NIPS
2001
15 years 5 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi
125
Voted
NIPS
2003
15 years 5 months ago
Optimal Manifold Representation of Data: An Information Theoretic Approach
We introduce an information theoretic method for nonparametric, nonlinear dimensionality reduction, based on the infinite cluster limit of rate distortion theory. By constraining...
Denis V. Chigirev, William Bialek
NIPS
2004
15 years 5 months ago
Proximity Graphs for Clustering and Manifold Learning
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
Miguel Á. Carreira-Perpiñán, ...