Diffusion Strategies For In-Network Principal Component Analysis
Résumé
This paper deals with the principal component analysis in networks, where it is improper to compute the sample covariance matrix. To this end, we derive several in-network strategies to estimate the principal axes, including noncooperative and cooperative (diffusion-based) strategies. The performance of the proposed strategies is illustrated on diverse applications, including image processing and dimensionality reduction of time series in wireless sensor networks.
Mots clés
unsupervised learning
in-network principal component analysis
cooperative diffusion-based strategy
covariance matrix
Principal component analysis
network
Convergence
Eigenvalues and eigenfunctions
Time series analysis
Wireless sensor networks
Cost function
Index Terms-Principal component analysis
distributed processing
adaptive learning
Covariance matrices
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...