7 accepted papers
The present paper develops a graph-based sampling and consensus (GraphSAC) approach to effectively detect anomalous nodes in large-scale graphs. GraphSAC randomly draws sub-sets of nodes, and relies on graph-aware criteria to judiciously filter out sets contaminated by anomalous nodes, before employ…
The present work introduces a simple scheme for active classification of data using unsupervised ensembles of classifiers. Uncertainty sampling, with different uncertainty measures, is evaluated for data selection, while an online expectation maximization algorithm is derived to estimate model param…
Channel gain cartography relies on sensor measurements to construct maps providing the attenuation profile between arbitrary transmitter-receiver locations. Existing approaches capitalize on tomographic models, where shadowing is the weighted integral of a spatial loss field (SLF) depending on the p…
The present work introduces methods for sampling and inference for the purpose of semi-supervised classification over the nodes of a graph. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that relatio…
The deluge of networked big data motivates the development of computation- and communication-efficient network information processing algorithms. In this paper, we propose two data-adaptive censoring strategies that significantly reduce the computation and communication costs of the distributed recu…
In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper puts forth approaches to render tracking of large-scale dynamic processes affordable, by processing a reduced…
Big data applications demand efficient solvers capable of providing accurate solutions to large-scale problems at affordable computational costs. Processing data sequentially, online algorithms offer attractive means to deal with massive data sets. However, they may incur prohibitive complexity in h…