Abstract
Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Clustering of the spatial maps is usually based on correlation matrices. This usually works well, although such a similarity matrix inherently can explain only a certain amount of the total variance contained in the highdimensional data where n is relatively small but p is large. For highdimensional space, it is reasonable to perform dimensionality reduction before clustering. In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering. This research revealed that the diffusion map based clustering worked as well as the more traditional methods, and produced more compact clusters when needed.
Original language  English 

Title of host publication  2013 IEEE International Workshop on Machine Learning for Signal Processing  Proceedings of MLSP 2013 
DOIs  
Publication status  Published  2013 
MoE publication type  A4 Article in a conference publication 
Event  IEEE International Workshop on Machine Learning for Signal Processing  Southampton, United Kingdom Duration: 22 Sep 2013 → 25 Sep 2013 Conference number: 16 
Publication series
Name  IEEE International Workshop on Machine Learning for Signal Processing 

ISSN (Print)  21610363 
Workshop
Workshop  IEEE International Workshop on Machine Learning for Signal Processing 

Abbreviated title  MLSP 
Country/Territory  United Kingdom 
City  Southampton 
Period  22/09/2013 → 25/09/2013 
Keywords
 clustering
 diffusion map
 dimensionality reduction
 functional magnetic resonance imaging (fMRI)
 independent component analysis
 spatial maps
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Aalto Neuroimaging Infrastructure
Veikko Jousmäki (Manager)
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