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» Kernel Principal Component Analysis
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NIPS
2008
15 years 1 months ago
Theory of matching pursuit
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound...
Zakria Hussain, John Shawe-Taylor
ICML
2003
IEEE
16 years 20 days ago
Kernel PLS-SVC for Linear and Nonlinear Classification
A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a ...
Roman Rosipal, Leonard J. Trejo, Bryan Matthews
SAS
2010
Springer
172views Formal Methods» more  SAS 2010»
14 years 10 months ago
Deriving Numerical Abstract Domains via Principal Component Analysis
Numerical Abstract Domains via Principal Component Analysis Gianluca Amato, Maurizio Parton, and Francesca Scozzari Universit`a di Chieti-Pescara – Dipartimento di Scienze We pro...
Gianluca Amato, Maurizio Parton, Francesca Scozzar...
ICONIP
2007
15 years 1 months ago
Principal Component Analysis for Sparse High-Dimensional Data
Abstract. Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solvi...
Tapani Raiko, Alexander Ilin, Juha Karhunen