Haut PDF Simulated Data for Linear Regression with Structured and Sparse Penalties
Simulated Data for Linear Regression with Structured and Sparse Penalties
... Introduction Simulated data are widely used to assess optimisation ...problem with real data and it proves to be a difficult problem even with simulated ...[4], for ...
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Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression
... a linear combination of the features), leading to non-convex objective ...considered for such approaches require alternate minimization [ Kolar and Sharpnack , 2012 ], possibly in an iterative fash- ...
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Convex relaxations of penalties for sparse correlated variables with bounded total variation
... estimation with a signal known to be sparse, spatially contiguous, and containing many highly correlated vari- ...to sparse prediction problems with correlated fea- tures, but lacks any ...
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Continuation of Nesterov’s Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging
... FISTA with fixed smoothing whose rate is O(1/ε) + O(1/ √ ε) [5], ...time and precision of the solution) several state-of-the-art optimiza- tion algorithms on both simulated and neuroimaging ...
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Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology
... performs sparse kernel regression based on a sparsity inducing prior on the weight parameters within a Bayesian ...input data and performs a trade-off between the number of basis (complexity ...
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Improving sparse recovery on structured images with bagged clustering
... neuroimaging data, brain re- gions can be linked with external ...particular, linear predictive models are interesting as their coefficients form brain maps that can be ...imaging data, their ...
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Prediction with high dimensional regression via hierarchically structured Gaussian mixtures and latent variables
... sub-clusters and/or outliers, violating the Gaussian assumption of the ...phenomenon with a numerical example in Section ...each linear map- ping even more ...some data points or lead to ...
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Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data
... of linear regression (LR) were compared, with regardto the quality of prediction and estimation and the robustness to deviations from underlying assumptions of normality, ...
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Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty
... in data analysis to uncover dominant patterns of variability within a ...a data set in a low-dimensional space, PCA’s inter- pretability remains ...account for the main variability in the brain ...
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High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression
... Compression and selection increase prediction ...compression and variable selection for prediction ...SGPLS and SPLS-DA perform better than logit-PLS, GPLS and PLS-DA respectively ...
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Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
... space, and General Linear Model (GLM) fit were performed with the SPM5 software ...affine and non-linear transformations) and not the one using unified ...addition, and ...
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Dual Extrapolation for Sparse Generalized Linear Models
... constant: for the Lasso and multitask Lasso, the Hessian does not depend on the current ...true for other GLM data fitting terms, e.g., Logistic regression, for which taking into ...
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On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection
... handled with care when the spectra are the predictors of a regression ...a regression method often leads to deteriorated ...that sparse regression methods, applied in the wavelet ...
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Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data
... genetic data x G tend to express through W, and thereby participate in the modulation of the vector α(x G ...], for which the codes are ...ours for each modality taken separately. For ...
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Bayesian Functional Linear Regression with Sparse Step Functions
... first and maybe most important hyperparameter is K, the number of intervals in the coefficient functions from the ...rainfall, and the number of observations, the value of K should stay small to remain ...the ...
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Multi-subject MEG/EEG source imaging with sparse multi-task regression
... (MNE) for instance are based on ` 2 Tikhonov regularization which leads to a linear solution ...a sparse collection of focal dipolar sources, hence their name “Minimum Current Estimates” ...[11] ...
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Supplementary material: Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging
... gradient and (ii) estimates weights that are strictly sparse because it does not require smoothing the sparsity-inducing ...any linear systems in P dimensions, or inverting very large matrices (XX ...
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Metric learning for structured data
... algorithms and design experiment on ...concern with the case of multiple relational tables between multiple entity ...starting with the selec- tion of similar, dissimilar ...entities and enti- ...
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Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
... EWA with a properly chosen prior is able to deal with the sparsity ...SOI for other common procedures of sparse ...bound for a large class of noise distributions, which is valid ...
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Learning A Tree-Structured Dictionary For Efficient Image Representation With Adaptive Sparse Coding
... curves for the Tree K- SVD method and the state-of-the-art ...representation, for complete (Fig.3), and overcomplete ...K-SVD with adaptive sparse coding (AdSC) is penal- ized by ...
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