Probabilistic Latent Variable Modeling Toolbox for Multisubject Data
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
calcICASSP17
demos
doc
functions
.gitignore
LICENSE.txt
README.md

README.md

PLVM - Probabilistic Latent Variable Modeling toolbox for multisubject data

The Probabilistic Latent Variable Modeling Toolbox for Multisubject Data holds a collection of latent variable algorithms implemented in Matlab™. The algorithms support the use of graphical processing units (GPUs) for high performance computing. All code can be used freely in research and other non-profit applications. If you publish results obtained with this toolbox we kindly ask that our and other relevant sources are properly cited.

This toolbox has been developed at:

The Technical University of Denmark, Department for Applied Mathematics and Computer Science, Section for Cognitive Systems.

The toolbox was developed in connection with the Brain Connectivity project at DTU (https://brainconnectivity.compute.dtu.dk/) .

Algorithms:

  • psFA
    • Probabilistic Sparse Factor Analysis (psFA). Models subject specific heteroscedastic feature/voxel noise.
  • psPCA
    • Probabilistic Sparse Principal Component Analysis (psPCA). Models subject specific homoscedastic noise.

Common algorithm properties

  • No parameters need to be set by default.
  • All models support modeling multiple subjects.
  • An approximate model solution is found using variational inference.
  • The evidence lowerbound is calculated (an approximation to log likelihood).
  • Estimating number of components using Automatic Relevance Determination (ARD).
  • Ability to individually turn off modeling aspects such as sparsity, ARD and noise modeling.

Demonstrators:

  • demo_psFA
    • Demostration on a toy example showing model settings and their effect
  • calcICASSP17
    • Synthetic experiments in the original psFA article (which is currently under review).