Discriminant Analysis for Multiway Data

Abstract : A multiway Fisher Discriminant Analysis (MFDA) formulation is presented in this paper. The core of MFDA relies on the structural constraint imposed to the discriminant vectors in order to account for the multiway structure of the data. This results in a more parsimonious model than that of Fisher Discriminant Analysis (FDA) performed on the unfolded data table. Moreover, computational and overfitting issues that occur with high dimensional data are better controlled. MFDA is applied to predict the long term recovery of patients after traumatic brain injury from multi-modal brain Magnetic Resonance Imaging. As compared to FDA, MFDA clearly tracks down the discrimination areas within the white matter region of the brain and provides a ranking of the contribution of the neuroimaging modalities. Based on cross validation, the accuracy of MFDA is equal to 77% against 75% for FDA.
Document type :
Book sections
Complete list of metadatas

Cited literature [6 references]  Display  Hide  Download

https://hal-centralesupelec.archives-ouvertes.fr/hal-01235812
Contributor : Alexandra Siebert <>
Submitted on : Monday, November 30, 2015 - 4:54:41 PM
Last modification on : Thursday, March 21, 2019 - 2:44:01 PM
Long-term archiving on : Tuesday, March 1, 2016 - 3:31:03 PM

File

Bookchapter Springer.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01235812, version 1

Citation

Gisela Lechuga, Laurent Le Brusquet, Vincent Perlbarg, Louis Puybasset, Damien Galanaud, et al.. Discriminant Analysis for Multiway Data. Springer Proceedings in Mathematics & Statistics, 2016, The Multiple Facets of Partial Least Squares and Related Methods. ⟨hal-01235812⟩

Share

Metrics

Record views

423

Files downloads

964