Publications related to Feature selection

Using Cardio-Respiratory Signals to Recognize Emotions Elicited by Watching Music Video Clips

Jean-Marc Vesin, Leila Mirmohamadsadeghi, Ashkan Yazdani

The automatic recognition of human emotions from physiological signals is of increasing interest in many applications. Images with high emotional content have been shown to alter signals such as the electrocardiogram~(ECG) and the respiration among many ot ...

IEEE2016

Isotropic Resolution in Fluorescence Imaging by Single Particle Reconstruction

Michaël Unser, Paul Guichard, Denis Fortun, Ning Chu

Low axial resolution is a major limitation of fluorescence imaging modalities. We propose a methodology to achieve high isotropic resolution by reconstructing fluorescence volumes from observations of multiple particle replicates with different orientation ...

IEEE2016

Incremental Consistency Guarantees for Replicated Objects

Rachid Guerraoui, Dragos-Adrian Seredinschi, Matej Pavlovic

Programming with replicated objects is difficult. Developers must face the fundamental trade-off between consistency and performance head on, while struggling with the complexity of distributed storage stacks. We introduce Correctables, a novel abstraction ...

2016

Automatic and computationally efficient method for model selection in inertial sensor calibration

Jan Skaloud, Stéphane Guerrier

The identification and selection of a small set of models that are able to well describe and predict the error signals coming from IMU sensors is of utmost importance to improve the navigation precision of these devices. For this reason, in this paper we p ...

2015

A simple model-based approach to variable selection in classification and clustering

Anthony Christopher Davison, Vahid Partovi Nia

Clustering and classification of replicated data is often performed using classical techniques that inappropriately treat the data as unreplicated, or by complex modern ones that are computationally demanding. In this paper, we introduce a simple approach ...

Wiley-Blackwell2015

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

Satoshi Takahama

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade-offs, but t ...

Wiley-Blackwell2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Transfer Learning through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

In this work we study the binary transfer learning problem involving 10^2 -10^3 sources. We focus on how to select sources from the large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer lea ...

Idiap2015

Multitask diffusion LMS with sparsity-based regularization

Ali H. Sayed, Roula Nassif

In this work, a diffusion-type algorithm is proposed to solve multitask estimation problems where each cluster of nodes is interested in estimating its own optimum parameter vector in a distributed manner. The approach relies on minimizing a global mean-sq ...

IEEE2015

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Bugra Tekin, Roberto Rigamonti, Amos Sironi

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, mak ...

Ieee Computer Soc2015

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Using Cardio-Respiratory Signals to Recognize Emotions Elicited by Watching Music Video Clips

Isotropic Resolution in Fluorescence Imaging by Single Particle Reconstruction

Incremental Consistency Guarantees for Replicated Objects

Automatic and computationally efficient method for model selection in inertial sensor calibration

A simple model-based approach to variable selection in classification and clustering

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Transfer Learning through Greedy Subset Selection

Multitask diffusion LMS with sparsity-based regularization

Learning Separable Filters

Transfer Learning through Greedy Subset Selection

A simple model-based approach to variable selection in classification and clustering

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

Automatic and computationally efficient method for model selection in inertial sensor calibration

Incremental Consistency Guarantees for Replicated Objects

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Multitask diffusion LMS with sparsity-based regularization

Learning Separable Filters

Using Cardio-Respiratory Signals to Recognize Emotions Elicited by Watching Music Video Clips

Isotropic Resolution in Fluorescence Imaging by Single Particle Reconstruction