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PAA
2008
8 years 8 months ago
Distance-based discriminant analysis method and its applications
This paper proposes a method of finding a discriminative linear transformation that enhances the data's degree of conformance to the compactness hypothesis and its inverse. Th...
Serhiy Kosinov, Thierry Pun
KAIS
2006
121views more  KAIS 2006»
8 years 8 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
JMLR
2008
169views more  JMLR 2008»
8 years 8 months ago
Multi-class Discriminant Kernel Learning via Convex Programming
Regularized kernel discriminant analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. Its performance depends on the selection of kernel...
Jieping Ye, Shuiwang Ji, Jianhui Chen
JEI
2008
135views more  JEI 2008»
8 years 8 months ago
Face authentication using a hybrid approach
This paper presents a hybrid approach to face-feature extraction based on the trace transform and the novel kernel partial-least-squares discriminant analysis (KPA). The hybrid app...
Vitomir Struc, France Mihelic, Nikola Pavesic
CSDA
2006
96views more  CSDA 2006»
8 years 8 months ago
Analysis of new variable selection methods for discriminant analysis
Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller sub...
Joaquín A. Pacheco, Silvia Casado, Laura N&...
CSDA
2006
87views more  CSDA 2006»
8 years 8 months ago
Choice of B-splines with free parameters in the flexible discriminant analysis context
Flexible discriminant analysis (FDA) is a general methodology which aims at providing tools for multigroup non linear classification. It consists in a nonparametric version of dis...
Christelle Reynès, Robert Sabatier, Nicolas...
BMCBI
2006
201views more  BMCBI 2006»
8 years 8 months ago
Gene selection algorithms for microarray data based on least squares support vector machine
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao
GECCO
2008
Springer
174views Optimization» more  GECCO 2008»
8 years 9 months ago
Mask functions for the symbolic modeling of epistasis using genetic programming
The study of common, complex multifactorial diseases in genetic epidemiology is complicated by nonlinearity in the genotype-to-phenotype mapping relationship that is due, in part,...
Ryan J. Urbanowicz, Nate Barney, Bill C. White, Ja...
NIPS
2004
8 years 9 months ago
Efficient Kernel Discriminant Analysis via QR Decomposition
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Re...
Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladi...
IJCAI
2007
8 years 9 months ago
Locality Sensitive Discriminant Analysis
Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discove...
Deng Cai, Xiaofei He, Kun Zhou, Jiawei Han, Hujun ...
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