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Journal Article
A Review of Feature Reduction Techniques in Neuroimaging
2014
Overview
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.
Publisher
Springer US,Springer,Springer Nature B.V
Subject
/ Biological and medical sciences
/ Biomedical and Life Sciences
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ Computer science; control theory; systems
/ Data processing. List processing. Character string processing
/ Exact sciences and technology
/ Humans
/ Investigative techniques, diagnostic techniques (general aspects)
/ Learning and adaptive systems
/ Memory organisation. Data processing
/ Neuroimaging - statistics & numerical data
/ Pattern recognition. Digital image processing. Computational geometry
/ Radiodiagnosis. Nmr imagery. Nmr spectrometry
/ Review
/ Software
