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Projection pursuit discriminant analysis [PDF]
Computational Statistics & Data Analysis, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Implementing projection pursuit learning
IEEE Transactions on Neural Networks, 1996This 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.
Ying Zhao 0006, Christopher G. Atkeson
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Oblique Projection Matching Pursuit
Mobile Networks and Applications, 2016Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient.
Jian Wang 0016 +3 more
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Generalized projection pursuit regression
SIAM Journal on Scientific Computing, 1998Summary: Projection pursuit regression (PPR) can be used to estimate a smooth function of several variables from noisy and scattered data. The estimate is a sum of smoothed one-dimensional projections of the variables. This paper discusses an extension of PPR to exponential family distributions, called generalized projection pursuit regression (GPPR ...
Ole Christian Lingjærde, Knut Liestøl
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Projection pursuit autoregression and projection pursuit moving average
Proceedings of 1994 Workshop on Information Theory and Statistics, 2002Projection pursuit autoregression (MPPAR) and projection pursuit moving average (MPPMA) with multivariate polynomials as ridge functions in both cases are proposed in this paper. The L/sub 2/-convergence of the methods is proved. This paper also proposes two new algorithms for MPPAR and MPPMA.
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WIREs Computational Statistics, 2009
AbstractProjection pursuit is a technique for finding highly informative low‐dimensional projections of multivariate data for visual inspection by an analyst. When data of dimension m are reduced to dimension p (where typically p = 2 is the most useful for viewing by scatter plots), the method consists of defining a measure of information content in ...
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AbstractProjection pursuit is a technique for finding highly informative low‐dimensional projections of multivariate data for visual inspection by an analyst. When data of dimension m are reduced to dimension p (where typically p = 2 is the most useful for viewing by scatter plots), the method consists of defining a measure of information content in ...
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Neural Computation, 1993
We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on real-world problems.
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We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on real-world problems.
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Automatic Induction of Projection Pursuit Indices
IEEE Transactions on Neural Networks, 2010Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such projection techniques is the projection pursuit (PP).
Eduardo Rodríguez-Martínez +3 more
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Exploratory Projection Pursuit
1995“Projection Pursuit” (PP) stands for a class of exploratory projection techniques. This class contains methods designed for analyzing high dimensional data using low-dimensional projections. The main idea is to describe “interesting” projections by maximizing an objective function or projection pursuit index.
Sigbert Klinke, Jörg Polzehl
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IJCNN-91-Seattle International Joint Conference on Neural Networks, 2002
A learning model based on a nonparametric statistical technique, projection pursuit regression, is studied. Projection pursuit is a nonparametric statistical technique to find interesting low-dimensional projections of high-dimensional data sets. Projection pursuit regression approximates a function of q variables by a sum of nonlinear functions of ...
Y. Zhao, C.G. Atkeson
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A learning model based on a nonparametric statistical technique, projection pursuit regression, is studied. Projection pursuit is a nonparametric statistical technique to find interesting low-dimensional projections of high-dimensional data sets. Projection pursuit regression approximates a function of q variables by a sum of nonlinear functions of ...
Y. Zhao, C.G. Atkeson
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