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/) .
- MultiSubjectAA
- MSAA with heteroscedastic noise in the first dimension.
- MultiSubjectAA_T
- 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.
- demoMSAA,demoMSAA_T
- Demostrates the algorithms and their optional parameters.
- demoVisualizeAA
- 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 [1]. The extension to heteroscedastic noise and ability to model multiple subjects was introduced by Hinrich et al. [2]. The solution of AA using projected gradient descent and the FurthestSum initialization was proposed by Mørup and Hansen[3]. While these reference provides the basis for this implementation of MSAA there are other interesting approaches in AA which could be used.
- [1] Cutler, A., & Breiman, L. (1994). Archetypal analysis. Technometrics, 36(4), 338-347.
- [2] 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.
- [3] Mørup, M., & Hansen, L. K. (2012). Archetypal analysis for machine learning and data mining. Neurocomputing, 80, 54-63.