arXiv research

A novel approach ODAR detects outliers for clustering.

A novel unsupervised outlier detection method using Randomized PCA Forest.

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…

New outlier detection method using graph Laplacian spectrum boosts performance.

New framework detects outliers in non-IID categorical data.

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…

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…

Generates synthetic data for benchmarking unsupervised outlier detection.

A new method detects outliers using ensembles of Dirichlet process mixtures.

PyODDS automates outlier detection for new data sources.

Paper tackles outlier detection in signals modeled by generative models with theoretical guarantees.

KOD detects outliers in high-dimensional data.

Integrates outlier detection into neural networks for improved performance.

A novel density-based approach QC detects outliers in data with high precision.

A novel semi-supervised outlier detection model detects anomalies with few labels.

Bayesian method detects outliers and uncertain points in data.

Outlier detection is a fundamental task in data mining and has many applications including detecting errors in databases. While there has been extensive prior work on methods for outlier detection, modern datasets often have sizes that are beyond the ability of commonly used methods to process the data within a reasona…

One-Class Boundary Peeling detects outliers efficiently and robustly.

Geometric approach improves functional outlier detection.

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…

This study surveys methods for detecting outliers in spatial data.

Despite tremendous progress in outlier detection research in recent years, the majority of existing methods are designed only to detect unconditional outliers that correspond to unusual data patterns expressed in the joint space of all data attributes. Such methods are not applicable when we seek to detect conditional …

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…

Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guar…

New algorithms detect outliers in high-dimensional data with arbitrary shapes.

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attribute…

Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.

Transforms distance-based outlier scores into interpretable probabilistic estimates.

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…

Proposes ATH for KPI anomaly detection based on local data properties.

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised mac…

ODIM detects outliers by under-fitting generative models, outperforming other methods.

Survey compares methods for generating artificial outliers.

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 new method detects and corrects outliers using optimal transport.

HLoOP detects outliers in hyperbolic 2-space.

Paper proposes DMGD for integrating outlier and community detection in graph embedding.

This work bridges outlier and drift detection by comparing inputs to a part of the reference distribution.

Outlier detection is a technique in data mining that aims to detect unusual or unexpected records in the dataset. Existing outlier detection algorithms have different pros and cons and exhibit different sensitivity to noisy data such as extreme values. In this paper, we propose a novel cluster-based outlier detection a…

Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into the point, contextual and collective outliers. The most important challenges in outlier detection include the thin boundary betwe…

Paper introduces detect-then-impute conformal prediction for cellwise outliers.

A contaminated mixture model detects outliers in multivariate functional data.

Rare data in a large-scale database are called outliers that reveal significant information in the real world. The subspace-based outlier detection is regarded as a feasible approach in very high dimensional space. However, the outliers found in subspaces are only part of the true outliers in high dimensional space, in…

We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}. In robust mean estimation the goal is to estimate the mean $μ$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\varepsilon$-fraction of which have been corrupted by a malicious…

AutoOD automates outlier detection using curiosity-guided search and self-imitation learning.

Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.

COPOD detects outliers efficiently and interpretable using copulas.

In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting. The variational parameters are obtained by sampling from the true posterior by gradient descent. We show our outlier detection results are comparable …