MSAA - MultiSubject Archetypal Analysis
The Multisubject Archetypal Analysis Toolbox holds several extensions to ordinary archetypal analysis. The algorithms are implemented in Matlab™ and 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/) .
- MSAA with heteroscedastic noise in the first dimension.
- MSAA with heteroscedastic noise in the second dimension.
Common algorithm properties
- Finds archetypes for multisubject data.
- The second dimension can have different length for each subject.
- Ability to individually turn off heteroscedastic noise modeling.
- The log likelihood is calculated.
- Demostrates the algorithms and their optional parameters.
- Demostrates how to visualize the found archetypes (Visualizations requires the VITLAB toolbox, avaliable at https://github.com/JesperLH/VITLAM).
Archetypal analysis was first proposed by Cutler and Brieman . The extension to heteroscedastic noise and ability to model multiple subjects was introduced by Hinrich et al. . The solution of AA using projected gradient descent and the FurthestSum initialization was proposed by Mørup and Hansen. While these reference provides the basis for this implementation of MSAA there are other interesting approaches in AA which could be used.
-  Cutler, A., & Breiman, L. (1994). Archetypal analysis. Technometrics, 36(4), 338-347.
-  Hinrich, J. L., Bardenfleth, S. E., Røge, R. E., Churchill, N. W., Madsen, K. H., & Mørup, M. (2016). Archetypal Analysis for Modeling Multisubject fMRI Data. IEEE Journal of Selected Topics in Signal Processing, 10(7), 1160-1171.
-  Mørup, M., & Hansen, L. K. (2012). Archetypal analysis for machine learning and data mining. Neurocomputing, 80, 54-63.