2025/02/15 by Daniel Winkle, Winkle, Daniel, Ingo Steinwart +3 · 1 citation
Computer Science · #FOS: Mathematics #Face and Expression Recognition #Neural Networks and Applications #Probability (math.PR) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2502.10772
openalex publication_date 2025/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the context of Gaussian conditioning, greedy algorithms iteratively select the most informative measurements, given an observed Gaussian random variable. However, the convergence analysis for conditioning Gaussian random variables remains an open problem. We adress this by introducing an operator M that allows us to transfer convergence rates of the observed Gaussian random variable approximation onto the conditional Gaussian random variable. Furthermore we apply greedy methods from approximation theory to obtain convergence rates. These greedy methods have already demonstrated optimal convergence rates within the setting of kernel based function approximation. In this paper, we establish an upper bound on the convergence rates concerning the norm of the approximation error of the conditional covariance operator.