Principal Components Analysis
Springer series in information sciencesPublished 1 January 2001
B. Roy Frieden
Citations115
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
A concept that is closely related to linear regression (preceding chapter) is principal components [15.1]. Linear regression addressed the question of how to fit a curve to one set of data, using a minimum number of factors. By contrast, the principal components problem asks how to fit many sets of data with a minimum number of curves. The problem is now of higher dimensionality. Specifically, can each of the data sets be represented as a weighted sum of a "best" set of curves? Each curve is called a "principal component" of the data sets.