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We are interested in information planning of structures represented by sparse graphical models where measurements correspond to a limited number of nodes. Choosing a set of measurements, which better describe spatiotemporal phenomena is a fundamental task whose optimal solution becomes intractable as the number of measurements grows. Krause et al. (2005) and Williams et al. (2007) have shown that...
We analyze the complexity of evaluating information rewards for measurement selection in sparse graphical models under the assumption that measurements are drawn from a limited number of nodes subject to a finite budget. Previous analyses [1, 2, 3] exploit the submodular property of conditional mutual information to demonstrate that greedy measurement selection come with near-optimal guarantees As...
Optimal measurement selection for inference is combinatorially complex and intractable for large scale problems. Under mild technical conditions, it has been proven that greedy heuristics combined with conditional mutual information rewards achieve performance within a factor of the optimal. Here we provide conditions under which cost-penalized mutual information may achieve similar guarantees. Specifically,...
Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the “curse of dimensionality” by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification...
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