NDLI: A Review of Feature Reduction Techniques in Neuroimaging

Content Provider	Springer Nature Link
Author	Mwangi, Benson Tian, Tian Siva Soares, Jair C.
Copyright Year	2013
Abstract	Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.
Starting Page	229
Ending Page	244
Page Count	16
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	12
Issue Number	2
e-ISSN	15590089
Language	English
Publisher	Springer US
Publisher Date	2013-09-08
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Feature reduction Feature selection Dimensionality reduction Machine learning Multivariate Predictive Modeling Neuroimaging Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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