Projection Pursuit Regression
نویسندگان
چکیده
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منابع مشابه
On the Use of Projection Pursuit Constraints for Training Neural Networks
\Ve present a novel classifica t.ioll and regression met.hod that combines exploratory projection pursuit. (unsupervised traiuing) with projection pursuit. regression (supervised t.raining), t.o yield a. nev,,' family of cost./complexity penalLy terms . Some improved generalization properties are demonstrat.ed on real \vorld problems.
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Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. Various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...
متن کاملCombining Exploratory Projection Pursuit and Projection Pursuit Regression with Application to Neural Networks
Parameter estimation becomes difficult in high-dimensional spaces due to the increasing sparseness of the data. Therefore, when a low-dimensional representation is embedded in the data, dimensionality reduction methods become useful. One such method-projection pursuit regression (Friedman and Stuetzle 1981 (PPR)-is capable of performing dimensionality reduction by composition, namely, it constr...
متن کاملLatent Factor Prediction Pursuit for Rank Deficient Regressors
In simulation studies Latent Factor Prediction Pursuit outperformed classical reduced rank regression methods. The algorithm described so far for Latent Factor Prediction Pursuit had two shortcomings: It was only implemented for situations where the explanatory variables were of full colum rank. Also instead of the projection matrix only the regression matrix was calculated. These problems are ...
متن کاملThe Partially Linear Regression Model: Monte Carlo Evidence from the Projection Pursuit Regression Approach
In a partially linear regression model with a high dimensional unknown component we find an estimator of the parameter of the linear part based on projection pursuit methods to be considerably more efficient than the standard density weighted kernel estimator.
متن کاملSome Approximation Properties of Projection Pursuit Learning Networks
This paper will address an important question in machine learning: What kind of network architectures work better on what kind of problems? A projection pursuit learning network has a very similar structure to a one hidden layer sigmoidal neural network. A general method based on a continuous version of projection pursuit regression is developed to show that projection pursuit regression works ...
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