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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Related publications (33)

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Robust Data-Driven Dynamic Programming

Daniel Kuhn, Grani Adiwena Hanasusanto

In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions ar ...

2013

Towards Estimation of Frontcrawl Energy Expenditure Using the Wearable Aquatic Movement Analysis System (WAMAS)

Kamiar Aminian, Grégoire Millet, Farzin Dadashi, Florent Crettenand

Inertial measurement unit (IMU) is a promising tool in the quantification of energy expenditure for human on-land activities, though has never been deployed before to calculate the aquatic activities energy expenditure. Investigating the factors that influ ...

IEEE2013

Towards Estimation of Front-crawl Energy Expenditure Using the Wearable Aquatic Movement Analysis System (WAMAS)

Kamiar Aminian, Grégoire Millet, Farzin Dadashi, Florent Crettenand

Ieee2013

Kernel regression for real-time building energy analysis

Matthew Brown

This study proposes a new technique for real-time building energy modelling and event detection using kernel regression. We show that this technique can exceed the performance of conventional neural network algorithms, and do so by a large margin when the ...

Taylor & Francis Ltd2012

Integration of Urban Structures in Point Process Analysis

Loïc Gasser

Traditionally, spatial analysis of point pattern has been mostly focused on Euclidean space. As many human related phenomena take place on a network, the assumption of a continuous isotropic space fails to describe events which actually occur on a one-dime ...

2011

SimpleMKL

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated predictor in supervised learning settings. For the support vector machine, an efficient and general multiple kernel learning algorithm, based on semi-infinite linear ...

2008

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

Matthias Seeger

We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, ...

Massachusetts Institute of Technology Press2008

Hierarchical Penalization

Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...

2007

Hierarchical Penalization

IDIAP2007

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Matthias Seeger

We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We ...

2007