Non-negative Distributed Regression for Data Inference in Wireless Sensor Networks
Résumé
Wireless sensor networks are designed to perform on inference the environment that they are sensing. Due to the inherent physical characteristics of systems under investigation, non-negativity is a desired constraint that must be imposed on the system parameters in some real-life phenomena sensing tasks. In this paper, we propose a kernel-based machine learning strategy to deal with regression problems. Multiplicative update rules are derived in this context to ensure the non-negativity constraints to be satisfied. Considering the tight energy and bandwidth resource, a distributed algorithm which requires only communication between neighbors is presented. Synthetic data managed by heat diffusion equations are used to test the algorithms and illustrate their tracking capacity.
Mots clés
non-negativity
sparsity
operating system kernels
learning (artificial intelligence)
Inference algorithms
Convergence
Signal processing algorithms
Wireless sensor networks
Heating
Cost function
regression analysis
non-negative distributed regression
data inference
kernel-based machine learning strategy
distributed algorithm
synthetic data
heat diffusion equations
Kernel
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...