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Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists.

, , , , , , and . NeuroImage, (2020)

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A New Feature Selection Method Based on Stability Theory - Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data., , and . MLINI, volume 7263 of Lecture Notes in Computer Science, page 51-59. Springer, (2011)Localizing and Comparing Weight Maps Generated from Linear Kernel Machine Learning Models., , , , , and . PRNI, page 124-127. IEEE, (2013)Utilizing temporal information in fMRI decoding: Classifier using kernel regression methods., , , , , and . NeuroImage, 58 (2): 560-571 (2011)Dynamic discrimination analysis: A spatial-temporal SVM., , and . NeuroImage, 36 (1): 88-99 (2007)Sparse network-based models for patient classification using fMRI., , , , , , and . NeuroImage, (2015)Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data., , , and . J. Cogn. Neurosci., 21 (5): 890-904 (2009)TransductGAN: a Transductive Adversarial Model for Novelty Detection., , and . CoRR, (2022)Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients., , , , , , , , , and 10 other author(s). NeuroImage, (2017)Multivariate Effect Ranking via Adaptive Sparse PLS., , , , and . PRNI, page 25-28. IEEE Computer Society, (2015)Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis., , , , , , and . CoRR, (2018)