An application of PCA on uncertainty of prediction

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015. Principal component analysis (PCA) has been widely used in many applications. In this paper, we present the problem of computational complexity in prediction, which increases as more input of predicting ev...

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Bibliographic Details
Main Author:	Santi Phithakkitnukoon
Format:	Book Series
Published:	2018
Subjects:	Computer Science
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961366765&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54358
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Institution:	Chiang Mai University

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spelling	th-cmuir.6653943832-543582018-09-04T10:12:25Z An application of PCA on uncertainty of prediction Santi Phithakkitnukoon Computer Science © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015. Principal component analysis (PCA) has been widely used in many applications. In this paper, we present the problem of computational complexity in prediction, which increases as more input of predicting event’s information is provided. We use the information theory to show that the PCA method can be applied to reduce the computational complexity while maintaining the uncertainty level of the prediction. We show that the percentage increment of uncertainty is upper bounded by the percentage increment of complexity. We believe that the result of this study will be useful for constructing predictive models for various applications, which operate with high dimensionality of data. 2018-09-04T10:12:25Z 2018-09-04T10:12:25Z 2015-01-01 Book Series 18678211 2-s2.0-84961366765 10.1007/978-3-319-15392-6_14 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961366765&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54358
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science
spellingShingle	Computer Science Santi Phithakkitnukoon An application of PCA on uncertainty of prediction
description	© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015. Principal component analysis (PCA) has been widely used in many applications. In this paper, we present the problem of computational complexity in prediction, which increases as more input of predicting event’s information is provided. We use the information theory to show that the PCA method can be applied to reduce the computational complexity while maintaining the uncertainty level of the prediction. We show that the percentage increment of uncertainty is upper bounded by the percentage increment of complexity. We believe that the result of this study will be useful for constructing predictive models for various applications, which operate with high dimensionality of data.
format	Book Series
author	Santi Phithakkitnukoon
author_facet	Santi Phithakkitnukoon
author_sort	Santi Phithakkitnukoon
title	An application of PCA on uncertainty of prediction
title_short	An application of PCA on uncertainty of prediction
title_full	An application of PCA on uncertainty of prediction
title_fullStr	An application of PCA on uncertainty of prediction
title_full_unstemmed	An application of PCA on uncertainty of prediction
title_sort	application of pca on uncertainty of prediction
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961366765&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54358
_version_	1681424305969168384

An application of PCA on uncertainty of prediction

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