6533b85bfe1ef96bd12bb74d
RESEARCH PRODUCT
Diffusion map for clustering fMRI spatial maps extracted by Indipendent Component Analysis
Tapani RistaniemiPetri ToiviainenTuomo SipolaElvira BratticoFengyu CongAsoke K. NandiVinoo Allurisubject
FOS: Computer and information sciencesDiffusion (acoustics)Computer sciencediffusion mapMachine Learning (stat.ML)02 engineering and technologycomputer.software_genreMachine Learning (cs.LG)Computational Engineering Finance and Science (cs.CE)Correlation03 medical and health sciencesTotal variation0302 clinical medicineStatistics - Machine LearningVoxel0202 electrical engineering electronic engineering information engineeringComputer Science - Computational Engineering Finance and ScienceCluster analysisdimensionality reductionta113spatial mapsbusiness.industryDimensionality reductionfunctional magnetic resonance imaging (fMRI)Pattern recognitionIndependent component analysisSpectral clusteringComputer Science - Learningindependent component analysista6131020201 artificial intelligence & image processingArtificial intelligenceDYNAMICAL-SYSTEMSbusinesscomputer030217 neurology & neurosurgeryclusteringdescription
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 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2013-01-01 |