MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers (LDA, Logistic Regression, SVM, kernel FDA, Naive Bayes, ensemble methods) and regression models (ridge, kernel ridge), using modern optimization algorithms. High-lev...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neuroscience 2020-06, Vol.14, p.289-289
Main Author: Treder, Matthias S.
Format: Article
Language:English
Subjects:
Bayesian analysis
Classification
decoding
Experiments
Houses
Interfaces
Light
Machine learning
Medical imaging
Multivariate analysis
MVPA
Neuroimaging
Neuroscience
Optimization algorithms
regression
Regression analysis
regularization
Statistical methods
Support vector machines
Variance analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers (LDA, Logistic Regression, SVM, kernel FDA, Naive Bayes, ensemble methods) and regression models (ridge, kernel ridge), using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g. time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance. It is modular, easily extendable, and is shipped with sample data and example scripts. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox.
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2020.00289