Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Tuomo Sipola, Fengyu Cong, Tapani Ristaniemi, Vinoo Alluri, Petri Toiviainen, Elvira Brattico, Asoke K. Nandi
2013 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  
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
more » ... e total variance contained in the high-dimensional data where n is relatively small but p is large. For high-dimensional 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.
doi:10.1109/mlsp.2013.6661923 dblp:conf/mlsp/SipolaCRATBN13 fatcat:7oae6vazozbarfs7eojmmmmevq