Diffusion Strategies For In-Network Principal Component Analysis
Proc. 24th IEEE workshop on Machine Learning for Signal Processing (MLSP)
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.