A Review of Feature Reduction Techniques in Neuroimaging

被引:0
作者
Benson Mwangi
Tian Siva Tian
Jair C. Soares
机构
[1] UT Houston Medical School,UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences
[2] University of Houston,Department of Psychology
[3] University of Texas Health Science Center at Houston,Department of Psychiatry and Behavioral Sciences
来源
Neuroinformatics | 2014年 / 12卷
关键词
Feature reduction; Feature selection; Dimensionality reduction; Machine learning; Multivariate; Predictive Modeling; Neuroimaging;
D O I
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中图分类号
学科分类号
摘要
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.
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页码:229 / 244
页数:15
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