Journal ArticleDOI
Exploratory Projection Pursuit
TLDR
A new projection pursuit algorithm for exploring multivariate data is presented that has both statistical and computational advantages over previous methods and the emphasis here is on the discovery of nonlinear effects such as clustering or other general nonlinear associations among the variables.Abstract:
A new projection pursuit algorithm for exploring multivariate data is presented that has both statistical and computational advantages over previous methods. A number of practical issues concerning its application are addressed. A connection to multivariate density estimation is established, and its properties are investigated through simulation studies and application to real data. The goal of exploratory projection pursuit is to use the data to find low- (one-, two-, or three-) dimensional projections that provide the most revealing views of the full-dimensional data. With these views the human gift for pattern recognition can be applied to help discover effects that may not have been anticipated in advance. Since linear effects are directly captured by the covariance structure of the variable pairs (which are straightforward to estimate) the emphasis here is on the discovery of nonlinear effects such as clustering or other general nonlinear associations among the variables. Although arbitrary ...read more
Citations
More filters
Journal ArticleDOI
Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
Journal ArticleDOI
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI
Fast and robust fixed-point algorithms for independent component analysis
TL;DR: Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Journal ArticleDOI
Survey of clustering algorithms
Rui Xu,Donald C. Wunsch +1 more
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
References
More filters
Book
Introduction to multivariate analysis
TL;DR: In this article, the multivariate normal distribution is used for principal component analysis and multivariate analysis of covariance and related topics, as well as multi-dimensional scaling and cluster analysis.
Journal ArticleDOI
Hedonic housing prices and the demand for clean air
TL;DR: In this article, the authors investigated the methodological problems associated with the use of housing market data to measure the willingness to pay for clean air, using a hedonic housing price model and data for the Boston metropolitan area.
Journal ArticleDOI
A Projection Pursuit Algorithm for Exploratory Data Analysis
Jerome H. Friedman,John W. Tukey +1 more
TL;DR: An algorithm for the analysis of multivariate data is presented and is discussed in terms of specific examples to find one-and two-dimensional linear projections of multivariable data that are relatively highly revealing.