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SUMMARY:Fast validation of LDA classifiers for cross-validation and permut
 ation testing + new MVPA toolbox - Matthias Treder (University of Birmingh
 am)
DTSTART:20180305T123000Z
DTEND:20180305T133000Z
UID:TALK100270@talks.cam.ac.uk
CONTACT:Johan Carlin
DESCRIPTION:In multivariate pattern analysis (MVPA) of neuroimaging data\,
  it is often important to establish whether a given classification perform
 ance is statistically significant. Unfortunately\, one of the most popular
  approaches\, permutation testing\, is computationally expensive\, since a
  classifier has to be trained and tested thousands of times. For Linear Di
 scriminant Analysis (LDA) classifiers\, this problem can be circumvented b
 y exploiting its relationship with linear regression. An update rule can b
 e used to calculate the classifier outputs in each cross-validation fold w
 ithout ever re-training the classifier. Remarkably\, since the hat matrix 
 is independent of the class labels\, this approach immediately extends to 
 permutations. The approach can be generalised to multi-class LDA via the n
 otion of optimal scoring. The update rule allows for drastic computational
  improvements especially in large feature spaces. Analyses using simulatio
 ns and a publicly available MEG dataset (Wakeman and Henson\, 2015) demons
 trate speed improvements of several orders of magnitude over the classical
  re-training approach while providing identical results.\n\nThe function w
 ill be implemented in MVPA-Light (github.com/treder/MVPA-Light)\, an easy-
 to-use toolbox on multivariate pattern classification in MATLAB. The toolb
 ox provides a fast interface for cross-validation\, searchlight analysis\,
  time classification and time x time generalisation. So far\, Linear Discr
 iminant Analysis (LDA) with shrinkage regularisation\, logistic regression
  with L2 regularisation\, linear and nonlinear Support Vector Machines\, m
 ulti-class LDA\, and ensemble methods have been implemented. To provide a 
 fast\, out-of-the-box solution\, MVPA-Light uses custom implementations of
  popular optimisation algorithms such as Dual Coordinate Descent.
LOCATION:Lecture Theatre\, MRC Cognition and Brain Sciences Unit\, 15 Chau
 cer Road\, Cambridge
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