# Haut PDF Simulated Data for Linear Regression with Structured and Sparse Penalties

### Simulated Data for Linear Regression with Structured and Sparse Penalties

**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

**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- ...

11

### Convex relaxations of penalties for sparse correlated variables with bounded total variation

**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 ...

23

### Continuation of Nesterov’s Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging

**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 ...

12

### Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

**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 ...

10

### Improving sparse recovery on structured images with bagged clustering

**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 ...

5

### Prediction with high dimensional regression via hierarchically structured Gaussian mixtures and latent variables

**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 ...

24

### Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data

**linear**

**regression**(LR) were compared,

**with**regardto the quality of prediction

**and**estimation

**and**the robustness to deviations from underlying assumptions of normality, ...

25

### Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty

**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 ...

14

### High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression

**and**selection increase prediction ...compression

**and**variable selection

**for**prediction ...SGPLS

**and**SPLS-DA perform better than logit-PLS, GPLS

**and**PLS-DA respectively ...

19

### Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

**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**...

20

### Dual Extrapolation for Sparse Generalized Linear Models

**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 ...

28

### On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection

**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 ...

46

### Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data

**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**...

12

### Bayesian Functional Linear Regression with Sparse Step Functions

**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 ...

26

### Multi-subject MEG/EEG source imaging with sparse multi-task regression

**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] ...

25

### Supplementary material: Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging

**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 ...

35

### Metric learning for structured data

**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- ...

169

### Sparse Regression Learning by Aggregation and Langevin Monte-Carlo

**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 ...

25

### Learning A Tree-Structured Dictionary For Efficient Image Representation With Adaptive Sparse Coding

**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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