6,259 research outputs found
Diffusion Adaptation Strategies for Distributed Estimation over Gaussian Markov Random Fields
The aim of this paper is to propose diffusion strategies for distributed
estimation over adaptive networks, assuming the presence of spatially
correlated measurements distributed according to a Gaussian Markov random field
(GMRF) model. The proposed methods incorporate prior information about the
statistical dependency among observations, while at the same time processing
data in real-time and in a fully decentralized manner. A detailed mean-square
analysis is carried out in order to prove stability and evaluate the
steady-state performance of the proposed strategies. Finally, we also
illustrate how the proposed techniques can be easily extended in order to
incorporate thresholding operators for sparsity recovery applications.
Numerical results show the potential advantages of using such techniques for
distributed learning in adaptive networks deployed over GMRF.Comment: Submitted to IEEE Transactions on Signal Processing. arXiv admin
note: text overlap with arXiv:1206.309
Sparse Distributed Learning Based on Diffusion Adaptation
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201
Learning to Race through Coordinate Descent Bayesian Optimisation
In the automation of many kinds of processes, the observable outcome can
often be described as the combined effect of an entire sequence of actions, or
controls, applied throughout its execution. In these cases, strategies to
optimise control policies for individual stages of the process might not be
applicable, and instead the whole policy might have to be optimised at once. On
the other hand, the cost to evaluate the policy's performance might also be
high, being desirable that a solution can be found with as few interactions as
possible with the real system. We consider the problem of optimising control
policies to allow a robot to complete a given race track within a minimum
amount of time. We assume that the robot has no prior information about the
track or its own dynamical model, just an initial valid driving example.
Localisation is only applied to monitor the robot and to provide an indication
of its position along the track's centre axis. We propose a method for finding
a policy that minimises the time per lap while keeping the vehicle on the track
using a Bayesian optimisation (BO) approach over a reproducing kernel Hilbert
space. We apply an algorithm to search more efficiently over high-dimensional
policy-parameter spaces with BO, by iterating over each dimension individually,
in a sequential coordinate descent-like scheme. Experiments demonstrate the
performance of the algorithm against other methods in a simulated car racing
environment.Comment: Accepted as conference paper for the 2018 IEEE International
Conference on Robotics and Automation (ICRA
Contributions to anomaly detection and correction in co-evolving data streams via subspace learning
During decades, estimation and detection tasks in many Signal Processing and Communications applications have been significantly improved by using subspace and component-based techniques. More recently, subspace methods have been adopted in many hot topics such as Machine Learning, Data Analytics or smart MIMO communications, in order to have a geometric interpretation of the problem. In that way, the Subspace-based algorithms often arise new approaches for already-explored problems, while offering the valuable advantage of giving interpretability to the procedures and solutions. On the other hand, in those recent hot topics, one may also find applications where the detection of unwanted or out-of-the-model artifacts and outliers is crucial. To this extend, we were previously working in the domain of GNSS PPP, detecting phase ambiguities, where we found motivation into the development of novel solutions for this application. After considering the applications and advantages of subspace-based approaches, this work will be focused on the exploration and extension of the ideas of subspace learning in the context of anomaly detection, where we show promising and original results in the areas of anomaly detection and subspace-based anomaly detection, in the form of two new algorithms: the Dual Ascent for Sparse Anomaly Detection and the Subspace-based Dual Ascent for Anomaly Detection and Tracking
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