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2010 IEEE International Workshop on Machine Learning for Signal Processing
Principal component analysis (PCA) and other multivariate methods have proven to be useful in a variety of engineering and science fields. PCA is commonly used for dimensionality reduction. PCA has also proven to be useful in functional magnetic resonance imaging (fMRI) research where it is used to decompose the fMRI data into components which can be associated with biological processes. In this thesis, a smooth version of PCA, derived from a maximum likelihood framework, is developed. A firstdoi:10.1109/mlsp.2010.5589208 fatcat:63zezl74y5cfzmnltk3e4ljrza