62 citations · 115 across the 3 of their papers we have counts for
3 papers
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions
Barnabas Poczos, Liang Xiong, Jeff Schneider
Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. The existing methods usua…
Kernels on Sample Sets via Nonparametric Divergence Estimates
Danica J. Sutherland, Liang Xiong, Barnabás Póczos +1
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algor…
Classification of Stellar Spectra with LLE
Scott F. Daniel, Andrew J. Connolly, Jeff Schneider +2
We investigate the use of dimensionality reduction techniques for the classification of stellar spectra selected from the SDSS. Using local linear embedding (LLE), a technique that…