Publications related to Lasso (statistics)

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word×time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequenti ...

Springer2013

Inverse Solutions for Brain Computer Interface

José del Rocio Millán Ruiz, Mohit Kumar Goel

Inverse solution allows to estimate sources that generate a given scalp EEG topography. Recently, it has been used in Brain Computer Interfaces (BCIs) to extract robust features based on the hypothesis that projection onto the source space (high dimensiona ...

EPFL2013

Grassmannian Regularized Structured Multi-View Embedding for Image Classification

Xinchao Wang

Images are usually represented by features from multiple views, e.g., color and texture. In image classification, the goal is to fuse all the multi-view features in a reasonable manner and achieve satisfactory classification performance. However, the featu ...

Institute of Electrical and Electronics Engineers2013

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupt ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Learning Separable Filters with Shared Parts

Bugra Tekin

Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally demanding and impractical to use ...

2013

Separable Filter Learning with Tensor Decomposition

Bugra Tekin

Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally de- manding and impractical to us ...

2013

Iterative FBP For Improved Reconstruction Of X-Ray Differential Phase-Contrast Tomograms

Michaël Unser, Cédric René Jean Vonesch, Peter Modregger, Masih Nilchian, Marco Stampanoni

X-ray differential phase-contrast tomography is a recently-developed modality for the imaging of low-contrast biological samples. Its mathematical model is based on the first derivative of the Radon transform and the images, in practice, are reconstructed ...

IEEE2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Volkan Cevher, Anastasios Kyrillidis

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

IEEE2013

Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy

Michaël Unser, Cédric René Jean Vonesch, Hagay Kirshner

Super-resolution localization microscopy relies on sparse activation of photo-switchable probes. Such activation, however, introduces limited temporal resolution. High-density imaging overcomes this limitation by allowing several neighboring probes to be a ...

IEEE2013

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A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Inverse Solutions for Brain Computer Interface

Grassmannian Regularized Structured Multi-View Embedding for Image Classification

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Learning Separable Filters with Shared Parts

Separable Filter Learning with Tensor Decomposition

Iterative FBP For Improved Reconstruction Of X-Ray Differential Phase-Contrast Tomograms

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy

Iterative FBP For Improved Reconstruction Of X-Ray Differential Phase-Contrast Tomograms

Grassmannian Regularized Structured Multi-View Embedding for Image Classification

Learning Separable Filters with Shared Parts

A Sequential Topic Model for Mining Recurrent Activities from Long Term Video Logs

Separable Filter Learning with Tensor Decomposition

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Inverse Solutions for Brain Computer Interface

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy