Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data col…
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48 results for “outlier removal”
A new method detects outliers in dirty data using a leave-out strategy.
problem Outliers in training data skew support vector machine performance.
method Leave-out strategy: temporarily omit one candidate at a time for outlier detection.
result The leave-out strategy improves outlier detection compared to existing methods.
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…
A new method detects and corrects outliers using optimal transport.
problem Outliers in data can skew estimation results, leading to inaccurate conclusions.
method Optimal transport with a concave cost function for outlier detection and correction.
result The method effectively identifies and corrects outliers, improving estimation accuracy.
Paper proposes a new method for selective inference in robust regression.
problem Statistical inference after removing outliers identified by robust methods.
method Conditional SI using piecewise-linear homotopy continuation.
result Proposed method is applicable to a wide class of robust regression and outlier detection methods.
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…
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…
PRAE identifies outliers and reconstructs inliers in autoencoders.
problem Accurately identifying anomalies in data.
method Probabilistic Robust AutoEncoder (PRAE) approach.
result PRAE effectively removes outliers and reconstructs inliers.
New method estimates mean from noisy data with few outliers.
problem Estimate mean from noisy data with few outliers.
method Iterative multi-filtering approach.
result Achieves near-optimal error with practical efficiency.
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.
Method matches noisy remote sensing images robustly.
problem Matching noisy remote sensing images.
method Combining attention mechanism with feature enhancement.
result More efficient and accurate matches achieved.
Christoffel function characterizes the corruption a bounded-degree certificate cannot remove in robust halfspace learning.
problem Robust halfspace learning under malicious noise
method Sum-of-Squares degree of outlier-removal certificate
result Christoffel function bounds the corruption a bounded-degree certificate cannot remove
Proposes AE for robust PCA, improving robustness to outliers.
problem PCA's sensitivity to outliers.
method Angular Embedding (AE) and Truncated Angular Embedding (TAE).
result AE/TAE outperforms state-of-the-art RPCA methods.
Paper improves DP-ERM for binary linear classification with large-margin subsets.
problem Differentially private binary linear classification with large-margin subsets.
method Efficient -DP algorithm with empirical zero-one risk bound.
result Improved empirical zero-one risk bound for binary linear classification.
We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder …
Paper cleans option price datasets by removing outliers.
problem Unusual option prices in datasets.
method Statistical techniques to identify and remove outliers.
result Removes option prices violating no arbitrage assumption.
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the ou…
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in order to fit the outliers. To overcome this problem, data often needs to be pre-processed to remove outliers, though this is difficult for a…
Improved LDA with capped l_{2,1}-norm reduces outlier sensitivity.
problem Outliers and noise sensitivity in classical LDA.
method Introducing capped l_{2,1}-norm and proposing CLDA.
result CLDA effectively removes outliers and suppresses noise.
We analyze a new robust method for the reconstruction of probability distributions of observed data in the presence of output outliers. It is based on a so-called gradient conjugate prior (GCP) network which outputs the parameters of a prior. By rigorously studying the dynamics of the GCP learning process, we derive an…
A new robust Wasserstein distance is proposed to handle outliers in probability distributions.
problem Outliers in probability distributions make Wasserstein distances sensitive and impractical.
method Introduces a new outlier-robust Wasserstein distance .
result Achieves strong robust estimation guarantees under the Huber -contamination model.
The paper analyzes how conformal prediction works with contaminated reference data.
problem The impact of contamination on the validity and power of conformal prediction methods.
method The paper analyzes the impact of contamination on the validity of conformal methods and proposes a data-cleaning framework to enhance power.
result The proposed data-cleaning framework can effectively enhance power while maintaining type-I error control.
Heavy-tailed outliers are more resilient to robust estimation than adversarial ones.
problem Developing robust estimators for data with outliers.
method Analyzing the relationship between adversarial and heavy-tailed outlier models.
result Optimal estimators for heavy-tailed outliers are also optimal for adversarial settings, but not vice versa.
We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems.…
HSNLD solves robust Hankel recovery efficiently and robustly.
problem Robust Hankel recovery of sparse outliers and missing entries.
method Hankel Structured Newton-Like Descent (HSNLD) algorithm.
result HSNLD achieves linear convergence independent of the condition number.
The trimming scheme with a prefixed cutoff portion is known as a method of improving the robustness of statistical models such as multivariate Gaussian mixture models (MG- MMs) in small scale tests by alleviating the impacts of outliers. However, when this method is applied to real- world data, such as noisy speech pro…
Online TERM improves robustness and fairness in streaming data.
problem Streaming data's lack of worst-case fairness and robustness in ERM.
method Proposes an online TERM formulation to balance average-case accuracy with worst-case fairness and robustness.
result Negative tilting effectively suppresses outlier influence, positive tilting improves recall with minimal precision loss.
Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either changing the network architecture or augmenting the training set with adversarial examples, but both have inherent limitations. Motivated by recent research that shows outliers in the traini…
Novel LRMC tackles missing data and outliers in large-scale low-rank data recovery.
problem Missing data and extreme outliers in low-rank data analysis.
method Learned Robust Matrix Completion (LRMC) using deep unfolding and flexible neural network framework.
result LRMC achieves optimum performance with low computational complexity and linear convergence.
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lies in a low-dimensional subspace that can change with time, albeit slowly. The goal is to track this changing subspace over time in the presence of sparse outliers. We develop and study …
A new method for robust matrix completion overcomes limitations of existing approaches.
problem Robust matrix completion from corrupted entries, especially under overparameterization and ill-conditioning.
method Factorization-based iterative algorithm combining Gauss-Newton linearization and outlier removal.
result Theoretical guarantees of exact recovery for suitable assumptions.
This paper proposes a new RWO-Sampling (Random Walk Over-Sampling) based on graphs for imbalanced datasets. In this method, two schemes based on under-sampling and over-sampling methods are introduced to keep the proximity information robust to noises and outliers. After constructing the first graph on minority class, …
We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers. We prove that the se…
New method corrects motion artifacts in MR images without paired data.
problem Lack of paired data for supervised training in deep learning for MR motion correction.
method Outlier-rejecting bootstrap subsampling and aggregation, using optimal transport cycleGAN.
result Outperforms existing deep learning methods in correcting motion artifacts from TSM.
We study the connections between spectral clustering and the problems of maximum margin clustering, and estimation of the components of level sets of a density function. Specifically, we obtain bounds on the eigenvectors of graph Laplacian matrices in terms of the between cluster separation, and within cluster connecti…
Study improves robustness of Bayesian inference for cognitive models.
problem Outliers and contaminants affect parameter estimation in cognitive models.
method Robustness of parameter estimation using amortized Bayesian inference (ABI) with neural networks.
result Introducing contaminants from a Cauchy distribution increases robustness.
Improved robust regression algorithms with faster runtime and better estimation rates.
problem Statistical regression problems under strong contamination model.
method Nearly-linear time algorithms using robust gradient descent and Sever framework.
result Improved estimation rates and runtime compared to state-of-the-art.
Transformer model removes noise from light curves efficiently.
problem Challenges in processing astrophysical light curves due to noise.
method Denoising Time Series Transformer (DTST) model trained with masked objective.
result DTST model excels at removing noise and outliers in time series datasets.
Time series analysis is used to understand and predict dynamic processes, including evolving demands in business, weather, markets, and biological rhythms. Exponential smoothing is used in all these domains to obtain simple interpretable models of time series and to forecast future values. Despite its popularity, expon…
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Taskscs.LG
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…
We discuss a common suspicion about reported financial data, in 10 industrial sectors of the 6 so called "main developing countries" over the time interval [2000-2014]. These data are examined through Benford's law first significant digit and through distribution distances tests. It is shown that several visually anoma…
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithm…
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.
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.
SRMD uses random features for efficient time-frequency analysis.
problem Efficiently analyzing time-series data with low computational cost.
method Sparse Random Mode Decomposition (SRMD) constructs a sparse approximation to the spectrogram.
result SRMD outperforms other methods in signal representation, outlier removal, and mode decomposition.
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…