Functional Projection Pursuit
نویسنده
چکیده
This article describes the adaption of exploratory projection pursuit for use with functional data. The aim is to nd \interesting" projections of functional data: e.g. to separate curves into meaningful clusters. Functional data are projected onto low-dimensional subspaces determined by a projection function using a suitable inner product. Such a projection is rapidly computed by representing data and projection function in terms of a suitable orthogonal basis. Both Fourier and wavelet bases are considered and their advantages and disadvantages outlined. The concepts of interpretable projection solutions , centring and sphering are also discussed. Two examples are presented: one simulated and one real concerning the core temperature of a group of infants that sleep with and without their mothers. The aim for the real data is to nd interesting projections that may separate the two groups.
منابع مشابه
انجام یک مرحله پیش پردازش قبل از مرحله استخراج ویژگی در طبقه بندی داده های تصاویر ابر طیفی
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 کاملRobust Functional Principal Components : a Projection - Pursuit Approach
In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection–pursuit with different smoothing methods. Consistency of the estimators are shown u...
متن کاملA Note on Projection Pursuit
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 implication of the difference is briefly discussed in the context of an application in which projection pur...
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 کامل