Functional Principal Component Analysis via Regularized Basis Expansion and Its Application
نویسندگان
چکیده
منابع مشابه
Functional Principal Component Analysis via Regularized Basis Expansion and Its Application
Recently, functional data analysis (FDA) has received considerable attention in various fields and a number of successful applications have been reported (see, e.g., Ramsay and Silverman (2005)). The basic idea behind FDA is the expression of discrete observations in the form of a function and the drawing of information from a collection of functional data by applying concepts from multivariate...
متن کاملRegularized Principal Component Analysis ∗
Given a set of signals, a classical construction of an optimal truncatable basis for optimally representing the signals, is the principal component analysis (PCA for short) approach. When the information about the signals one would like to represent is a more general property, like smoothness, a different basis should be considered. One example is the Fourier basis which is optimal for represen...
متن کاملSparse Principal Component Analysis via Regularized Low Rank Matrix Approximation
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout science and engineering. However, the principal components (PCs) can sometimes be difficult to interpret, because they are linear combinations of all the original variables. To facilitate interpretation, sparse PCA produces modified PCs with sparse loadings, i.e. loading...
متن کاملRegularized Principal Component Analysis for Spatial Data
Abstract: In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at p locations with n repeated measurements. While principal component analysis (PCA) is commonly applied to find the patterns, the eigenimages produced from PCA may be noisy or exhibit patterns that are not physically meaningful when p is large relative ...
متن کاملTropical Principal Component Analysis and its Application to Phylogenetics
Principal component analysis is a widely-used method for the dimensionality reduction of a given data set in a high-dimensional Euclidean space. Here we define and analyze two analogues of principal component analysis in the setting of tropical geometry. In one approach, we study the Stiefel tropical linear space of fixed dimension closest to the data points in the tropical projective torus; in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Japanese journal of applied statistics
سال: 2006
ISSN: 0285-0370,1883-8081
DOI: 10.5023/jappstat.35.1