190views more  NECO 2000»
8 years 8 months ago
Generalized Discriminant Analysis Using a Kernel Approach
We present a new method that we call Generalized Discriminant Analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is ...
G. Baudat, Fatiha Anouar
129views more  CVIU 2007»
8 years 8 months ago
Multi-view face and eye detection using discriminant features
Multi-view face detection plays an important role in many applications. This paper presents a statistical learning method to extract features and construct classifiers for multi-...
Peng Wang, Qiang Ji
124views more  CSDA 2004»
8 years 8 months ago
Fast and robust discriminant analysis
The goal of discriminant analysis is to obtain rules that describe the separation between groups of observations. Moreover it allows to classify new observations into one of the k...
Mia Hubert, Katrien van Driessen
152views more  TKDE 2008»
8 years 8 months ago
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
Linear Discriminant Analysis (LDA) has been a popular method for extracting features that preserves class separability. The projection functions of LDA are commonly obtained by max...
Deng Cai, Xiaofei He, Jiawei Han
121views more  TKDE 2008»
8 years 8 months ago
Kernel Uncorrelated and Regularized Discriminant Analysis: A Theoretical and Computational Study
Linear and kernel discriminant analyses are popular approaches for supervised dimensionality reduction. Uncorrelated and regularized discriminant analyses have been proposed to ove...
Shuiwang Ji, Jieping Ye
109views more  BIOINFORMATICS 2005»
8 years 8 months ago
Prediction error estimation: a comparison of resampling methods
In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future obser...
Annette M. Molinaro, Richard Simon, Ruth M. Pfeiff...
144views more  PR 2008»
8 years 8 months ago
Kernel quadratic discriminant analysis for small sample size problem
It is generally believed that quadratic discriminant analysis (QDA) can better fit the data in practical pattern recognition applications compared to linear discriminant analysis ...
Jie Wang, Konstantinos N. Plataniotis, Juwei Lu, A...
161views more  PR 2008»
8 years 8 months ago
A study on three linear discriminant analysis based methods in small sample size problem
In this paper, we make a study on three Linear Discriminant Analysis (LDA) based methods: Regularized Discriminant Analysis (RDA), Discriminant Common Vectors (DCV) and Maximal Ma...
Jun Liu, Songcan Chen, Xiaoyang Tan
8 years 8 months ago
Diagnosis of lung nodule using Moran's index and Geary's coefficient in computerized tomography images
This paper analyzes the application of Moran's index and Geary's coefficient to the characterization of lung nodules as malignant or benign in computerized tomography ima...
Erick Corrêa da Silva, Aristófanes Co...