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We propose a novel non-parametric adaptive outlier detection algorithm, called LPE, for high dimensional data based on score functions derived from nearest neighbor graphs on n-point nominal data. Outliers are predicted whenever the score of a test sample falls below ??, which is supposed to be the desired false alarm level. The resulting outlier detector is shown to be asymptotically optimal in that...
In this paper, we study distributed classification of targets in a large scale sensor network setting. Specifically, we consider sensor nodes which can measure only a part of the feature vector and whose communication capabilities are limited to only their neighbouring nodes. We formulate a distributed classification algorithm that learns the optimal (large-margin) hyperplane separating the two classes,...
This paper discusses the ranking of a set of objects when a possibly inconsistent set of pairwise preferences is given.We consider the task of ranking objects when pairwise preferences not only can contradict each other, but in general are not binary-meaning, for each pair of objects the preference is represented by a pair of non-negative numbers that sum up to one and can be viewed as a confidence...
The minimum connected dominating set (MCDS) of a given graph G is the smallest sub-graph of G such that every vertex in G belongs either to the sub-graph or is adjacent to a vertex of the sub-graph. Finding the MCDS in an arbitrary graph is a NP-Hard problem, and several approximation algorithms have been proposed for solving this problem in deterministic graphs, but to the best of our knowledge no...
As a supervised learning algorithm, the standard Gaussian processes has the excellent performance of classification. In this paper, we present a semi-supervised algorithm to learning a Gaussian process classifier, which incorporating a graph-based construction of semi-supervised kernels in the presence of labeled and unlabeled data, and expanding the standard Gaussian processes algorithm into the...
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
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