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ICML
2003
IEEE
14 years 6 months 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
KAIS
2006
121views more  KAIS 2006»
13 years 5 months ago
Using discriminant analysis for multi-class classification: an experimental investigation
Abstract. Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the...
Tao Li, Shenghuo Zhu, Mitsunori Ogihara
IDA
1999
Springer
13 years 9 months ago
Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization
A method for the linear discrimination of two classes has been proposed by us in 3 . It searches for the discriminant direction which maximizes the distance between the projected c...
Mayer Aladjem
ECCV
2004
Springer
14 years 7 months ago
Dimensionality Reduction by Canonical Contextual Correlation Projections
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
Marco Loog, Bram van Ginneken, Robert P. W. Duin
ICPR
2010
IEEE
13 years 7 months ago
Compressing Sparse Feature Vectors Using Random Ortho-Projections
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
Esa Rahtu, Mikko Salo, Janne Heikkilä