Distributed Database Kriging for Adaptive Sampling (D2KAS)

Distributed Adaptive Sampling for Kernel Matrix Approximation

Most kernel-based methods, such as kernel or Gaussian process regression, kernel PCA, ICA, or k-means clustering, do not scale to large datasets, because constructing and storing the kernel matrix Kn requires at least O(n2) time and space for n samples. Recent works [1, 9] show that sampling points with replacement according to their ridge leverage scores (RLS) generates small dictionaries of r...

Sampling Design Optimization for Space-Time Kriging

An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability

The estimation of system failure probabilities may be a difficult task when the values involved are very small, so that sampling-based Monte Carlo methods may become computationally impractical, especially if the computer codes used to model the system response require large computational efforts, both in terms of time and memory. This paper proposes a modification of an algorithm proposed in l...

Adaptive Query-Based Sampling of Distributed Collections

As part of a Distributed Information Retrieval system a description of each remote information resource, archive or repository is usually stored centrally in order to facilitate resource selection. The acquisition of precise resource descriptions is therefore an important phase in Distributed Information Retrieval, as the quality of such representations will impact on selection accuracy, and ul...

Distributed adaptive sampling using bounded-errors

This paper presents a communication/coordination/ processing architecture for distributed adaptive observation of a spatial field using a fleet of autonomous mobile sensors. One of the key difficulties in this context is to design scalable algorithms for incremental fusion of information across platforms robust to what is known as the “rumor problem”. Incremental fusion is in general based on a...