Friedman and Stuetzle (JASA, 1981) developed a methodology for modeling a response surface by the sum of general smooth functions of linear combinations of the predictor variables. Here multiplicative models for regression and categorical regression are explored. The construction of these models and their performance relative to additive models are examined. CHAPTER 0 INTRODUCTION In recent wor...

\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.

This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Emp...

Abstract: 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 data sets, most projections are close to uniform. Thus, informative summaries are ones d...

The present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). basis adaptation method and its accelerated algorithm polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional adapted specific quantities interest (QoI). paper one difficulty with these adaptations, namely...

The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified -means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. We show that clustering can reduce within-class variance and group pixels with similar correlati...

Principal Component Analysis is a technique often found to be useful for identifying structure in multivariate data. Although it has various characterizations (Rao 1964), the most familiar is as a variance-maximizing projection. Projection pursuit is a methodology for selecting low-dimensional projections of multivariate data by the optimization of some index of \interestingness" over all proje...