28,958 research outputs found
Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization
We consider the online and nonparametric detection of abrupt and persistent
anomalies, such as a change in the regular system dynamics at a time instance
due to an anomalous event (e.g., a failure, a malicious activity). Combining
the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method
with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we
propose a computationally efficient online anomaly detection method that is
applicable to high-dimensional datasets, and at the same time achieve a
near-optimum average detection delay performance for a given false alarm
constraint. We provide new insights to both GEM and CUSUM, including new
asymptotic analysis for GEM, which enables soft decisions for outlier
detection, and a novel interpretation of CUSUM in terms of the discrepancy
theory, which helps us generalize it to the nonparametric GEM statistic. We
numerically show, using both simulated and real datasets, that the proposed
nonparametric algorithm attains a close performance to the clairvoyant
parametric CUSUM test.Comment: to appear in IEEE International Symposium on Information Theory
(ISIT) 201
One-class classifiers based on entropic spanning graphs
One-class classifiers offer valuable tools to assess the presence of outliers
in data. In this paper, we propose a design methodology for one-class
classifiers based on entropic spanning graphs. Our approach takes into account
the possibility to process also non-numeric data by means of an embedding
procedure. The spanning graph is learned on the embedded input data and the
outcoming partition of vertices defines the classifier. The final partition is
derived by exploiting a criterion based on mutual information minimization.
Here, we compute the mutual information by using a convenient formulation
provided in terms of the -Jensen difference. Once training is
completed, in order to associate a confidence level with the classifier
decision, a graph-based fuzzy model is constructed. The fuzzification process
is based only on topological information of the vertices of the entropic
spanning graph. As such, the proposed one-class classifier is suitable also for
data characterized by complex geometric structures. We provide experiments on
well-known benchmarks containing both feature vectors and labeled graphs. In
addition, we apply the method to the protein solubility recognition problem by
considering several representations for the input samples. Experimental results
demonstrate the effectiveness and versatility of the proposed method with
respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification
Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN,
Vancouver, Canad
Graph ambiguity
In this paper, we propose a rigorous way to define the concept of ambiguity in the domain of graphs. In past studies, the classical definition of ambiguity has been derived starting from fuzzy set and fuzzy information theories. Our aim is to show that also in the domain of the graphs it is possible to derive a formulation able to capture the same semantic and mathematical concept. To strengthen the theoretical results, we discuss the application of the graph ambiguity concept to the graph classification setting, conceiving a new kind of inexact graph matching procedure. The results prove that the graph ambiguity concept is a characterizing and discriminative property of graphs. (C) 2013 Elsevier B.V. All rights reserved
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