Survey compares methods for generating artificial outliers.
problem Difficulty in detecting genuine outliers.
method Generates artificial outliers to approximate genuine ones.
result Variability in quality of generation approaches.
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48 results for “generating outliers”
Paper tackles outlier detection in signals modeled by generative models with theoretical guarantees.
problem Recovering signals from linear measurements with sparse outliers.
method Proposes an iterative ADMM algorithm and gradient descent algorithm for outlier detection using and squared norm minimization.
result Establishes theoretical recovery guarantees for signal reconstruction under sparse outliers.
Generates synthetic data for benchmarking unsupervised outlier detection.
problem Difficulty in benchmarking unsupervised outlier detection due to rare and varied outliers in real data.
method Proposes a generic process to generate synthetic data with insightful characteristics.
result Demonstrates practicality of the generic process through a benchmark with state-of-the-art detection methods.
ODIM detects outliers by under-fitting generative models, outperforming other methods.
problem Identifying outliers in unlabeled data without supervision.
method Develops ODIM, a method that uses under-fitted deep generative models to detect outliers.
result ODIM outperforms other methods in detecting outliers efficiently and accurately.
Transforms distance-based outlier scores into interpretable probabilistic estimates.
problem Difficult interpretation of distance-based outlier scores.
method Generic transformation of scores into probabilistic estimates using distance probability distributions.
result Probabilistic transformation improves interpretability without impacting detection performance.
New findings suggest deep generative models can misclassify outliers, requiring new evaluation methods.
problem Deep generative models often assign higher likelihood to outliers, challenging existing outlier detection methods.
method Analyzed the typical set and high-density region of DGMs, proposing a novel outlier test.
result Existing likelihood-based outlier tests may fail due to model calibration issues, not just misclassification.
Paper tackles outlier detection in multi-armed bandits, achieving high accuracy with reduced exploration costs.
problem Detecting outlier arms in multi-armed bandit settings.
method Proposes GOLD algorithm based on upper confidence bounds to identify generic outlier arms.
result Achieves 98% accuracy with 83% reduction in exploration cost compared to state-of-the-art techniques.
This paper considers the problem of recovering signals from compressed measurements contaminated with sparse outliers, which has arisen in many applications. In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers. We propose an iterative al…
Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect ou…
Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional spac…
Paper proposes methods to make OT robust to outliers.
problem Optimal transport is sensitive to outliers.
method Detect outliers using adversarial training, adjust transport cost based on classifier predictions.
result Outliers are detected and do not affect transport in experiments.
Study improves robustness and sparsity in linear regression with adversarial outliers and heavy-tailed noise.
problem Outliers and heavy-tailed noise in linear regression coefficients.
method Sharp concentration inequalities and generic chaining.
result Sharper error bounds under weaker assumptions.
Bayesian method detects outliers and uncertain points in data.
problem Detecting outliers and uncertain points in data using Bayesian methods.
method Generative model of data curation for aleatoric uncertainty, combining with epistemic uncertainty and outlier exposure.
result Principled Bayesian approach outperforms methods using aleatoric or epistemic uncertainty alone.
Enhances predictive models against misspecification and outliers.
problem Suboptimal generalization under misspecification and outliers.
method Combines PAC ensemble bounds with a generalized logarithm score function.
result Produces predictive distributions resistant to both misspecification and outliers.
We focus on the problem of unsupervised cell outlier detection and repair in mixed-type tabular data. Traditional methods are concerned only with detecting which rows in the dataset are outliers. However, identifying which cells are corrupted in a specific row is an important problem in practice, and the very first ste…
Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in gener…
A novel outlier score detects new road infrastructure images.
problem Detecting newly observed road infrastructure images.
method Entropy-based outlier score using directed nearest neighbor graphs.
result High potential of the proposed technique in identifying outliers.
A novel framework IMBoost improves outlier detection by leveraging the inlier memorization effect.
problem Challenges in unsupervised outlier detection, especially when inliers and outliers are not well-separated or form dense clusters.
method IMBoost framework that incorporates active learning to selectively acquire informative labels and explicitly reinforce the inlier memorization effect.
result IMBoost significantly outperforms state-of-the-art active outlier detection methods and requires less computational cost.
One-Class Boundary Peeling detects outliers efficiently and robustly.
problem Unsupervised outlier detection in diverse data distributions.
method One-Class Boundary Peeling uses flexible boundaries generated by one-class SVMs and iteratively peels them.
result One-Class Boundary Peeling outperforms state-of-the-art methods in synthetic data simulations.
RECol generates error columns to improve outlier detection.
problem Outlier detection in data with complex relationships.
method Generates reconstruction error columns for leave-one-out feature sets.
result Improves ROC-AUC and PR-AUC values of common outlier detection methods.
New algorithm detects outliers from rare abnormal data.
problem Detecting outliers from rare abnormal data in transfer learning.
method Meta-algorithm for transfer learning in outlier detection.
result Meta-algorithm yields strong guarantees and outperforms existing methods.
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effectstat.ML
ALTBI enhances outlier detection by maximizing the inlier-memorization effect.
problem Improving outlier detection models via optimization of inlier-memorization effect.
method ALTBI introduces two techniques: increasing mini-batch size and using adaptive threshold for truncated loss function.
result ALTBI achieves state-of-the-art performance in identifying outliers with lower computation costs.
Geometric approach improves functional outlier detection.
problem Detecting outliers in functional data sets.
method Developed a geometric perspective on functional manifold for outlier detection.
result Improved understanding and differentiation of outliers.
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure…
A novel approach ODAR detects outliers for clustering.
problem Outliers interfere with clustering algorithms, leading to unreliable results.
method Feature transformation to separate outliers and normal objects into distinct clusters.
result ODAR improves clustering accuracy on 7 out of 10 datasets.
Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion, outlier trimming, and post hoc outlier identification methods, with the forme…
This work describes an outlier detection procedure (named "OutlierTree") loosely based on the GritBot software developed by RuleQuest research, which works by evaluating and following supervised decision tree splits on variables, in whose branches 1-d confidence intervals are constructed for the target variable and pot…
Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, outliers are defined by the concept of cluster, which are recognized as the points belonging to none of the clusters. Unfortunately, most existing studies do…
New framework detects outliers in non-IID categorical data.
problem Existing outlier detection methods fail in non-IID data.
method Value-value graph-based representation and outlierness propagation.
result Significant improvement in AUC on complex data sets.
Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent and when they are defined by unusual combinations of multiple outcome variable values. In this paper, we develop and study a new conditiona…
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers do…
We consider the problem of clustering datasets in the presence of arbitrary outliers. Traditional clustering algorithms such as k-means and spectral clustering are known to perform poorly for datasets contaminated with even a small number of outliers. In this paper, we develop a provably robust spectral clustering algo…
Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying perfo…
A novel unsupervised outlier detection method using Randomized PCA Forest.
problem Unsupervised outlier detection in datasets.
method Randomized Principal Component Analysis (RPCA) Forest for deriving an outlier score.
result Superior performance compared to classical and state-of-the-art methods.
It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We…
Outlier detection (also known as anomaly detection or deviation detection) is a process of detecting data points in which their patterns deviate significantly from others. It is common to have outliers in industry applications, which could be generated by different causes such as human error, fraudulent activities, or …
HLoOP detects outliers in hyperbolic 2-space.
problem Detecting local outliers in hyperbolic 2-space.
method Combines nearest neighbor finding and probabilistic scoring in hyperbolic space.
result Promising results on WordNet dataset.
Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practical methods perform nonlinear ICA by solving a series of classification problems based on logistic regression. However, it is well-known that…
Proposes GMOTE for better handling imbalanced data.
problem Classification of imbalanced data.
method Generates minority class instances using Gaussian Mixture Model, considering outliers through Mahalanobis distance.
result Shows better accuracy and F1-Score when combined with CART or SVM.
Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anoma…
Blind Source Separation is a widely used technique to analyze multichannel data. In many real-world applications, its results can be significantly hampered by the presence of unknown outliers. In this paper, a novel algorithm coined rGMCA (robust Generalized Morphological Component Analysis) is introduced to retrieve s…
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the outlier model where each column of the data matrix is either an inlier or an outlier. Most of the existing methods for this model assumes either the knowledge of the dim…
Paper proposes a method to generate synthetic anomalies for robust anomaly detection.
problem Anomaly detection struggles with unbalanced data and rare anomalies.
method Two-level hierarchical latent space representation for feature distillation and synthesis.
result The method creates robust synthetic anomalies for training robust binary classifiers.
New outlier detection method using graph Laplacian spectrum boosts performance.
problem Detecting outliers in large datasets efficiently.
method Boosted outlier detection based on graph Laplacian spectrum.
result Outperforms existing methods on synthetic datasets.
We evaluate how modern outlier detection methods perform in identifying outliers in e-commerce conversion rate data. Based on the limitations identified, we then present a novel method to detect outliers in e-commerce conversion rate. This unsupervised method is made more business relevant by letting it automatically a…
Enhances clustering for functional data, robust to outliers.
problem Challenges of clustering infinite-dimensional functional data and outlier sensitivity.
method Extends OCLUST algorithm to handle functional data, trimming outliers.
result Strong performance in clustering and outlier identification on simulated and real-world datasets.
ECOD detects outliers without parameters, fast and simple.
problem Detecting outliers in large, high-dimensional datasets efficiently and interpretably.
method ECOD estimates empirical cumulative distribution functions per dimension, then computes tail probabilities and outlier scores.
result ECOD outperforms state-of-the-art methods in accuracy, efficiency, and scalability.
COPOD detects outliers efficiently and interpretable using copulas.
problem Outliers in multivariate data are hard to detect efficiently and interpretably.
method COPOD constructs an empirical copula to predict tail probabilities and identify outliers.
result COPOD outperforms existing methods in most cases and is computationally efficient.