Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 302 for “"Outlier"”.
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Outlier sequence detection using LZW
Includes bibliographical references (pages 85-90).
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Outlier detection for information networks
… recently has there been some work in the area of outlier detection for information network data. Outlier (or anomaly) detection is a very broad field and has been studied in the context of a large number of application domains. Many algorithms have been proposed for outlier detection in …
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New approaches for outlier detection
Outlier detection has relevance in many modern day contexts, including health care, engineering, data processing and analysis, credit card fraud, monitoring computer and internet intrusions and wearable personal health sensors. Outlier detection once represented a single pre-processing step, …
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Certifiable Outlier-Robust Geometric Perception
… to space autonomy. The ubiquitous existence of outliers —measurements that tell no or little information about the models to be estimated— makes it theoretically intractable to perform estimation with guaranteed optimality. Despite this theoretical intractability, safety-critical robotics …
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Outlier Resistant Model Robust Regression
… model. The method introduced here (termed Outlier Resistant Model Robust Regression (ORMRR)) addresses the situation that arises when one cannot assume well-behaved data that vary according to a Normal distribution. ORMRR is a blend of a robust parametric fit, such as M-estimation, with a …
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Generative Models Driven Graph Outlier Detection
… and communication systems. The detection of outliers in graph data—substructures that significantly deviate from the norm—is crucial for uncovering fraudulent activities, network vulnerabilities, and novel patterns. However, traditional outlier detection techniques often fall short of …
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Scalable Techniques for Trajectory Outlier Detection
… of data analysis operations such as trajectory outlier detection, which consists in the identification of those trajectories that behave much differently from the rest of the trajectories in a database. There are several time-critical applications such as traffic management systems, security …
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Relational Outlier Detection: Techniques and Applications
Nowadays, outlier detection has attracted growing interest. Unlike typical outlier detection problems, relational outlier detection focuses on detecting abnormal patterns in datasets that contain relational implications within each data point. Furthermore, different from the traditional outlier …
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The Chaco connection: Bonito style architecture in outlier communities
… great houses, appears in nearly 100 Chacoan outlier communities throughout the greater San Juan Basin. This study employs architectural evidence in an attempt to clarify the nature and scope of the relationship between Chacoan outliers and the sites in Chaco Canyon and to increase …
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Universal outlier hypothesis testing with applications to anomaly detection
Outlier hypothesis testing is studied in a universal setting. Multiple sequences of observations are collected, a small subset (possibly empty) of which are outliers. A sequence is considered an outlier if the observations in that sequence are distributed according to an “outlier” distribution, …
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Robust model selection and outlier detection in linear regressions
… study the problems of robust model selection and outlier detection in linear regression. The results of data analysis based on linear regressions are highly sensitive to model choice and the existence of outliers in the data. This thesis aims to help researchers to choose the correct model when …
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Gradient Subgroup Scanning for Distributionally and Outlier Robust Models
… approaches tend to show reduced performance when outliers are also present in the data. Unfortunately, existing methods for improving subgroup performance cannot be simply combined with prior approaches for excluding outliers, as they often directly conflict. This work proposes a method for …
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Information-based projection method for categorical clustering and outlier detection.
… projection method for categorical clustering and outlier detection.
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Text mining with word embedding for outlier and sentiment analysis
… Specifically, this study focuses on two tasks: outlier analysis and weakly-supervised sentiment analysis. Outlier analysis aims to identify documents that topically deviate from the majority of a given corpus. We develop an unsupervised generative model to identify frequent and representative …
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Graph-Theoretic Outlier Rejection: From Instance to Category-Level Perception
In this thesis, we study the problem of outlier pruning for robust estimation. Robust estimation is the workhorse for many perception problems, from object pose estimation to robot localization and mapping. In these problems, the robot has to estimate quantities of interest in the face of outliers. …
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Multivariate Outlier Mining Using Cluster Analysis: Case Study - National Health Interview Survey
Outlier mining is a fundamental issue in many statistical analyses, especially in multivariate cases. Outliers may exert undue influence on outcomes of the analysis. In most cases, it is a big challenge to reveal the pattern of the outliers and the "outlyingness". There are several approaches and …
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Reliable Robotic Perception: From Outlier-Robust Estimation to Task-Aware Runtime Monitoring
… algorithms tend to fail in the presence of many outliers and require extensive parameter tuning. This thesis presents a comprehensive exploration of outlier-robust estimation algorithms, perception monitoring, and risk assessment to enhance the robustness and safety of robots and autonomous …
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Outlier treatments using interolation on Malaysia tourist arrival forecasting: SARIMA and ANN approaches
Outliers are unusual observations that appear in a piece of data that are very different from the rest of the data. The presence of an outlier may directly affect the variance, the model parameters, and the overall estimation, especially during forecasting. To obtain an accurate forecast, any …
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