A Review of Feature Reduction Techniques in Neuroimaging

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 individ...

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Vydáno v:	Neuroinformatics (Totowa, N.J.) Ročník 12; číslo 2; s. 229 - 244
Hlavní autoři:	Mwangi, Benson, Tian, Tian Siva, Soares, Jair C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.04.2014 Springer Springer Nature B.V
Témata:	Applied sciences Artificial Intelligence Bioinformatics Biological and medical sciences Biomedical and Life Sciences Biomedicine Brain - physiology 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 Medical sciences Memory organisation. Data processing Nervous system Neuroimaging - methods Neuroimaging - statistics & numerical data Neurology Neurosciences Pattern recognition. Digital image processing. Computational geometry Radiodiagnosis. Nmr imagery. Nmr spectrometry Review Software Statistics as Topic Dimensionality reduction Predictive Modeling Neuroimaging Feature selection Machine learning Multivariate Feature reduction Central nervous system Modeling Encephalon Multidimensional analysis Voxel Selection criterion Age Medical application Computer vision Data analysis Redundancy Inference Data reduction Dimension reduction Discrimination Medical imagery Tridimensional image Artificial intelligence
ISSN:	1539-2791, 1559-0089, 1559-0089
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23
ISSN:	1539-2791 1559-0089 1559-0089
DOI:	10.1007/s12021-013-9204-3