Interactive Principal Component Analysis

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Siirtola , H , Säily , T & Nevalainen , T 2017 , Interactive Principal Component Analysis . in E Banissi (et al.) (ed.) , Proceedings of the 21st International Conference on Information Visualisation (IV 2017) . Information Visualization , IEEE Computer Society , Los Alamitos, California , pp. 416-421 , International Conference on Information Visualisation (IV 2017) , London , United Kingdom , 11/07/2017 .

Title: Interactive Principal Component Analysis
Author: Siirtola, Harri; Säily, Tanja; Nevalainen, Terttu
Other contributor: Banissi (et al.), Ebad
Contributor organization: Department of Modern Languages 2010-2017
Publisher: IEEE Computer Society
Date: 2017
Language: eng
Number of pages: 6
Belongs to series: Proceedings of the 21st International Conference on Information Visualisation (IV 2017)
Belongs to series: Information Visualization
ISBN: 978-1-5386-0831-9
ISSN: 2375-0138
Abstract: Principal Component Analysis (PCA) is an established and efficient method for finding structure in a multidimensional data set. PCA is based on orthogonal transformations that convert a set of multidimensional values into linearly uncorrelated variables called principal components.The main disadvantage to the PCA approach is that the procedure and outcome are often difficult to understand. The connection between input and output can be puzzling, a small change in input can yield a completely different output, and the user may often wonder if the PCA is doing the right thing.We introduce a user interface that makes the procedure and result easier to understand. We have implemented an interactive PCA view in our text visualization tool called Text Variation Explorer. It allows the user to interactively study the result of PCA, and provides a better understanding of the process.We believe that although we are addressing the problem of interactive principal component analysis in the context of text visualization, these ideas should be useful in other contexts as well.
Subject: 6121 Languages
text visualization
corpus linguistics
historical sociolinguistics
113 Computer and information sciences
information visualization
principal component analysis
interactive visualization
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: acceptedVersion
Grant number: 293009

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