Journal ArticleDOI
On feature selection, curse-of-dimensionality and error probability in discriminant analysis
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TLDR
The increase of the moments of the discriminant function induced by the curse-of-dimensionality is shown together with the effect of the threshold-based feature selection, which makes it possible to express the overall error probability in a closed form and view it as a function of a given threshold of selection.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2003-08-01. It has received 52 citations till now. The article focuses on the topics: Linear discriminant analysis & Optimal discriminant analysis.read more
Citations
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Positive approximation: An accelerator for attribute reduction in rough set theory
TL;DR: A theoretic framework based on rough set theory, called positive approximation, is introduced, which can be used to accelerate a heuristic process of attribute reduction, and several representative heuristic attribute reduction algorithms inrough set theory have been enhanced.
Journal ArticleDOI
Model-based clustering of high-dimensional data: A review
TL;DR: Existing softwares for model-based clustering of high-dimensional data will be reviewed, their practical use will be illustrated on real-world data sets and clustering methods based on variable selection are reviewed.
Journal ArticleDOI
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation
Qinghua Hu,Zongxia Xie,Daren Yu +2 more
TL;DR: A simple and efficient hybrid attribute reduction algorithm based on a generalized fuzzy-rough model based on fuzzy relations is introduced and the technique of variable precision fuzzy inclusion in computing decision positive region can get the optimal classification performance.
Journal ArticleDOI
High-dimensional data clustering
TL;DR: In this paper, a family of Gaussian mixture models designed for high-dimensional data which combine the ideas of subspace clustering and parsimonious modeling are presented, which give rise to a clustering method based on the expectation-maximization algorithm which is called High-dimensional Data Clustering (HDDC).
References
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Book
Linear statistical inference and its applications
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Book
Continuous univariate distributions
TL;DR: Continuous Distributions (General) Normal Distributions Lognormal Distributions Inverse Gaussian (Wald) Distributions Cauchy Distribution Gamma Distributions Chi-Square Distributions Including Chi and Rayleigh Exponential Distributions Pareto Distributions Weibull Distributions Abbreviations Indexes
OtherDOI
Generalized Additive Models
Trevor Hastie,Robert Tibshirani +1 more
TL;DR: The generalized additive model (GA) as discussed by the authors is a generalization of the generalized linear model, which replaces the linear model with a sum of smooth functions in an iterative procedure called local scoring algorithm.