
Factor Analysis of Mixed Data for Anomaly Detection
Anomaly detection aims to identify observations that deviate from the ty...
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Detecting independence of random vectors II. Distance multivariance and Gaussian multivariance
We introduce two new measures for the dependence of n > 2 random variabl...
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Multicriteria Similaritybased Anomaly Detection using Pareto Depth Analysis
We consider the problem of identifying patterns in a data set that exhib...
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ATD: Anomalous Topic Discovery in High Dimensional Discrete Data
We propose an algorithm for detecting patterns exhibited by anomalous cl...
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Anomaly Detection and Localisation using Mixed Graphical Models
We propose a method that performs anomaly detection and localisation wit...
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Approximate Inference in Structured Instances with Noisy Categorical Observations
We study the problem of recovering the latent ground truth labeling of a...
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Selectivity in Probabilistic Causality: Drawing Arrows from Inputs to Stochastic Outputs
Given a set of several inputs into a system (e.g., independent variables...
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Compressed Anomaly Detection with Multiple Mixed Observations
We consider a collection of independent random variables that are identically distributed, except for a small subset which follows a different, anomalous distribution. We study the problem of detecting which random variables in the collection are governed by the anomalous distribution. Recent work proposes to solve this problem by conducting hypothesis tests based on mixed observations (e.g. linear combinations) of the random variables. Recognizing the connection between taking mixed observations and compressed sensing, we view the problem as recovering the "support" (index set) of the anomalous random variables from multiple measurement vectors (MMVs). Many algorithms have been developed for recovering jointly sparse signals and their support from MMVs. We establish the theoretical and empirical effectiveness of these algorithms at detecting anomalies. We also extend the LASSO algorithm to an MMV version for our purpose. Further, we perform experiments on synthetic data, consisting of samples from the random variables, to explore the tradeoff between the number of mixed observations per sample and the number of samples required to detect anomalies.
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