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The quantitative evaluation of functional neuroimaging expertiments: The NPAIRS data analysis framework. NeuroImage 11: S592
, 2000
"... We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPM ..."
Abstract
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Cited by 68 (17 self)
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We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training–test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility
On the regularization of canonical correlation analysis
- in Proceedings of the International Conference on Independent Component Analysis and Blind Source Separation (ICA2003), S
, 2003
"... By elucidating a parallel between canonical correlation anal-ysis (CCA) and least squares regression (LSR), we show how regularization of CCA can be performed and inter-preted in the same spirit as the regularization applied in ridge regression (RR). Furthermore, the results presented may have an im ..."
Abstract
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Cited by 8 (1 self)
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By elucidating a parallel between canonical correlation anal-ysis (CCA) and least squares regression (LSR), we show how regularization of CCA can be performed and inter-preted in the same spirit as the regularization applied in ridge regression (RR). Furthermore, the results presented may have an impact on the practical use of regularized CCA (RCCA). More specifically, a relevant cross validation cost function for train-ing the regularization parameter, naturally follows from the derivations. 1.
Non-negative partial least squares for meta-analytic parcellation: A functional atlas for the human brain
, 2006
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2. Review of PLS and its Modifications 3. PLS Regression 4. ”The Peculiar Shrinkage Properties ” of PLS Regression
"... • PLS- a class of techniques for modeling relations between blocks of observed variables by means of latent variables • Herman Wold’66,’75- NIPALS- to linearize models nonlinear in the parameters • Svante Wold et. al ’83- NIPALS extended for the overdetermined regression problems- PLS Regression • C ..."
Abstract
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• PLS- a class of techniques for modeling relations between blocks of observed variables by means of latent variables • Herman Wold’66,’75- NIPALS- to linearize models nonlinear in the parameters • Svante Wold et. al ’83- NIPALS extended for the overdetermined regression problems- PLS Regression • Chemometrics- strong latent variable structure
