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ICML
2003
IEEE
14 years 7 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 6 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 10 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 8 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 8 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