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KDD
2012
ACM
281views Data Mining» more  KDD 2012»
13 years 6 months ago
Active spectral clustering via iterative uncertainty reduction
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforw...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord...
135
Voted
BMCV
2000
Springer
15 years 8 months ago
The Spectral Independent Components of Natural Scenes
Abstract. We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was a...
Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski
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
AAAI
2007
15 years 5 months ago
Compact Spectral Bases for Value Function Approximation Using Kronecker Factorization
A new spectral approach to value function approximation has recently been proposed to automatically construct basis functions from samples. Global basis functions called proto-val...
Jeffrey Johns, Sridhar Mahadevan, Chang Wang
144
Voted
BMCBI
2010
124views more  BMCBI 2010»
15 years 3 months ago
A Dynamic Noise Level Algorithm for Spectral Screening of Peptide MS/MS Spectra
Background: High-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide...
Hua Xu, Michael A. Freitas