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Projection Pursuit Regression
 Journal of the American Statistical Association
, 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
Abstract

Cited by 550 (6 self)
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A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, does not require the definition of a metric in the predictor space, and lends itself to graphical interpretation.
Grand Tour and Projection Pursuit
 Journal of Computational and Graphical Statistics
, 1995
"... The grand tour and projection pursuit are two methods for exploring multivariate data. We show how to combine them into a dynamic graphical tool for exploratory data analysis, called a projection pursuit guided tour. This tool assists in clustering data when clusters are oddly shaped and in finding ..."
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Cited by 84 (21 self)
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The grand tour and projection pursuit are two methods for exploring multivariate data. We show how to combine them into a dynamic graphical tool for exploratory data analysis, called a projection pursuit guided tour. This tool assists in clustering data when clusters are oddly shaped and in finding
Projection pursuit density estimation
 Journal of the American Statistical Association
, 1984
"... The projection pursuit methodology is applied to the multivariate density estimation problem. The resulting nonparametric procedure is often less biased than kernel and near neighbor methods. In addition, graphical information is produced that can be used to help gain geometric insight into the mult ..."
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Cited by 73 (2 self)
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The projection pursuit methodology is applied to the multivariate density estimation problem. The resulting nonparametric procedure is often less biased than kernel and near neighbor methods. In addition, graphical information is produced that can be used to help gain geometric insight
A note on projection pursuit
, 2002
"... I provide a historic review of the forward and backward projection pursuit algorithms, previously thought to be equivalent, and point out an important difference between the two. In doing so, I correct a small error in the original exploratory projection pursuit paper (Friedman 1987). The implicatio ..."
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Cited by 2 (0 self)
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I provide a historic review of the forward and backward projection pursuit algorithms, previously thought to be equivalent, and point out an important difference between the two. In doing so, I correct a small error in the original exploratory projection pursuit paper (Friedman 1987
Implementing Projection Pursuit Learning
, 1996
"... This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations ..."
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Cited by 13 (1 self)
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This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis
ThreeDimensional Projection Pursuit
 J. Royal Statistical Society, Series C
, 1995
"... This article discusses various aspects of projection pursuit into three dimensions. The aim of projection pursuit is to find interesting linear combinations of variables in a multivariate data set. The precise definition of "interesting" is given later but clusters and other forms of nonl ..."
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Cited by 13 (0 self)
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This article discusses various aspects of projection pursuit into three dimensions. The aim of projection pursuit is to find interesting linear combinations of variables in a multivariate data set. The precise definition of "interesting" is given later but clusters and other forms of non
Projection Pursuit for Discrete Data
, 2008
"... This paper develops projection pursuit for discrete data using the discrete Radon transform. Discrete projection pursuit is presented as an exploratory method for finding informative low dimensional views of data such as binary vectors, rankings, phylogenetic trees or graphs. We show that for most ..."
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Cited by 4 (2 self)
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This paper develops projection pursuit for discrete data using the discrete Radon transform. Discrete projection pursuit is presented as an exploratory method for finding informative low dimensional views of data such as binary vectors, rankings, phylogenetic trees or graphs. We show
Logistic Response Projection Pursuit
, 1993
"... A highly flexible nonparametric regression model for predicting a response y given covariates x is the projection pursuit regression (PPR) model y = h(x) = fi 0 + P j fi j f j (ff T j x), where the f j are general smooth functions with mean zero and norm one, and P d k=1 ff 2 kj = 1. With a ..."
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Cited by 5 (2 self)
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A highly flexible nonparametric regression model for predicting a response y given covariates x is the projection pursuit regression (PPR) model y = h(x) = fi 0 + P j fi j f j (ff T j x), where the f j are general smooth functions with mean zero and norm one, and P d k=1 ff 2 kj = 1
Functional Projection Pursuit
 Computing Science and Statistics
, 1998
"... This article describes the adaption of exploratory projection pursuit for use with functional data. The aim is to find "interesting" projections of functional data: e.g. to separate curves into meaningful clusters. Functional data are projected onto lowdimensional subspaces determined by ..."
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Cited by 1 (0 self)
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This article describes the adaption of exploratory projection pursuit for use with functional data. The aim is to find "interesting" projections of functional data: e.g. to separate curves into meaningful clusters. Functional data are projected onto lowdimensional subspaces determined
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