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» Nonlinear Nonnegative Component Analysis
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ICPR
2002
IEEE
14 years 20 days ago
Determining a Suitable Metric when Using Non-Negative Matrix Factorization
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or partbased representation of o...
David Guillamet, Jordi Vitrià
ICASSP
2011
IEEE
12 years 11 months ago
Nonnegative 3-way tensor factorization via conjugate gradient with globally optimal stepsize
This paper deals with the minimal polyadic decomposition (also known as canonical decomposition or Parafac) of a 3way array, assuming each entry is positive. In this case, the low...
Jean-Philip Royer, Pierre Comon, Nadège Thi...
ICPR
2002
IEEE
14 years 8 months ago
Analyzing Non-Negative Matrix Factorization for Image Classification
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or partbased representation of o...
Bernt Schiele, David Guillamet, Jordi Vitrià...
ICANN
1997
Springer
13 years 12 months ago
Kernel Principal Component Analysis
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
BMCBI
2010
155views more  BMCBI 2010»
13 years 7 months ago
A flexible R package for nonnegative matrix factorization
Background: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face re...
Renaud Gaujoux, Cathal Seoighe