Multivariate classification of neuroimaging data with nested subclasses: Biased accuracy and implications for hypothesis testing.
Biological data sets are typically characterized by high dimensionality and low effect sizes. A powerful method for detecting systematic differences between experimental conditions in such multivariate data sets is multivariate pattern analysis (MVPA), particularly pattern classification. However, i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC6177201?pdf=render |