PCA based dimensionality reduction of MRI images for training support vector machine to aid diagnosis of bipolar disorder

This study aims to investigate how dimensionality reduction of neuroimaging data prior to training support vector machines (SVMs) affects the classification accuracy of bipolar disorder. This study uses principal component analysis (PCA) for dimensionality reduction. An open source data set of 19 bi...

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Bibliographic Details
Main Authors:	Chen, Beichen, Chen, Amy Jinxin
Format:	Others
Language:	English
Published:	KTH, Skolan för elektroteknik och datavetenskap (EECS) 2019
Subjects:	Bipolar disorder diagnosis computer-aided medical diagnosis SVM Support vector machine PCA Principal component analysis dimensionality reduction feature reduction neuroimaging MRI sMRI machine learning classification psychiatric disorders mental illness Bipolär sjukdom diagnotisering datorstödd medicinsk diagnotisering stödvektormaskin principalkomponentanalys magnetisk resonanstomografi MRT dimensionalitetsreduktion maskininlärning dimensionsreduktion klassificering psykiska sjukdomar Computer and Information Sciences Data- och informationsvetenskap
Online Access:	http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259621

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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259621

PCA based dimensionality reduction of MRI images for training support vector machine to aid diagnosis of bipolar disorder

Internet

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