Pattern classification - Brain Research Imaging Centre Edinburgh

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Neuroimaging data “feature vector” or “data point” <strong>Brain</strong> volume Problem:1000’s of features vs. 10’s of data points Possible solutions to dimensionality problem: – Feature selection strategies (e.g. ROIS, select only activated voxels) – (Searchlight) – Kernel Methods
Kernel approaches • Mathematical trick! powerful and unified framework (e.g. <strong>classification</strong> & regression) • Consist of two parts: - build the kernel matrix (mapping into the feature space) - train using the kernel matrix (designed to discover linear patterns in the feature space) • Advantages: - computational shortcut represent linear patterns efficiently in high dimensional space. - Using the dual representation with proper regularization efficient solution of ill-conditioned problems. • Examples Support Vector Machine (SVM), Gaussian Processes (GP), Kernel Ridge Regression (KRR),…
Page 1 and 2: Pattern Recognition for Neuroimagin
Page 3 and 4: Overview • Introduction - Univari
Page 5 and 6: BOLD signal Univariate vs. multivar
Page 7 and 8: Neuroimaging data “feature vector
Page 9 and 10: Pattern recognition framework Input
Page 11: voxel 2 Classification example L R
Page 15 and 16: Linear classifier • hyperplanes t
Page 17 and 18: Support Vector Machine SVM = “max
Page 19 and 20: SVM vs. GP SVM Hard binary classif
Page 21 and 22: Training set Test set … … …
Page 23 and 24: Confusion matrix & accuracy Confusi
Page 25 and 26: Regression MSE • LOO error in one
Page 29 and 30: Example of masks Linear machine Wei
Page 33 and 34: Design Between subjects •2 groups
Page 35 and 36: Design: Within subject • Block or
Page 37 and 38: Design: Within subject • Block or
Page 39 and 40: Multiclass problem Multiclass machi
Page 41: Conclusions Key points: • More se
Page 44: Thank you for your attention! Any q

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