300 research outputs found
Communication Network Design: Balancing Modularity and Mixing via Optimal Graph Spectra
By leveraging information technologies, organizations now have the ability to
design their communication networks and crowdsourcing platforms to pursue
various performance goals, but existing research on network design does not
account for the specific features of social networks, such as the notion of
teams. We fill this gap by demonstrating how desirable aspects of
organizational structure can be mapped parsimoniously onto the spectrum of the
graph Laplacian allowing the specification of structural objectives and build
on recent advances in non-convex programming to optimize them. This design
framework is general, but we focus here on the problem of creating graphs that
balance high modularity and low mixing time, and show how "liaisons" rather
than brokers maximize this objective
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly
detection which aims at recognizing anomalous aggregate behaviors of data
points. The OCSMMs generalize well-known one-class support vector machines
(OCSVMs) to a space of probability measures. By formulating the problem as
quantile estimation on distributions, we can establish an interesting
connection to the OCSVMs and variable kernel density estimators (VKDEs) over
the input space on which the distributions are defined, bridging the gap
between large-margin methods and kernel density estimators. In particular, we
show that various types of VKDEs can be considered as solutions to a class of
regularization problems studied in this paper. Experiments on Sloan Digital Sky
Survey dataset and High Energy Particle Physics dataset demonstrate the
benefits of the proposed framework in real-world applications.Comment: Conference on Uncertainty in Artificial Intelligence (UAI2013
Dirichlet Densifiers for Improved Commute Times Estimation
In this paper, we develop a novel Dirichlet densifier that can be used to increase the edge density in undirected graphs. Dirichlet densifiers are implicit minimizers of the spectral gap for the Laplacian spectrum of a graph. One consequence of this property is that they can be used improve the estimation of meaningful commute distances for mid-size graphs by means of topological modifications of the original graphs. This results in a better performance in clustering and ranking. To do this, we identify the strongest edges and from them construct the so called line graph, where the nodes are the potential q −step reachable edges in the original graph. These strongest edges are assumed to be stable. By simulating random walks on the line graph, we identify potential new edges in the original graph. This approach is fully unsupervised and it is both more scalable and robust than recent explicit spectral methods, such as the Semi-Definite Programming (SDP) densifier and the sufficient condition for decreasing the spectral gap. Experiments show that our method is only outperformed by some choices of the parameters of a related method, the anchor graph, which relies on pre-computing clusters representatives, and that the proposed method is effective on a variety of real-world datasets.M. Curado, F. Escolano and M.A. Lozano are funded by the projects TIN2015-69077-P and BES2013-064482 of the Spanish Government
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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