Understanding Principal Component Analysis Using a Visual Analytics Tool
BibTeX
@MISC{Jeong_understandingprincipal,
author = {Dong Hyun Jeong and Caroline Ziemkiewicz and William Ribarsky and Remco Chang},
title = {Understanding Principal Component Analysis Using a Visual Analytics Tool},
year = {}
}
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Abstract
Principle Component Analysis (PCA) is a mathematical procedure widely used in exploratory data analysis, signal processing, etc. However, it is often considered a black box operation whose results and procedures are difficult to understand. The goal of this paper is to provide a detailed explanation of PCA based on a designed visual analytics tool that visualizes the results of principal component analysis and supports a rich set of interactions to assist the user in better understanding and utilizing PCA. The paper begins by describing the relationship between PCA and single vector decomposition (SVD), the method used in our visual analytics tool. Then a detailed explanation of the interactive visual analytics tool, including advantages and limitations, is provided. 1.
