A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study surveys methods for detecting outliers in spatial data.
A novel unsupervised outlier detection method using Randomized PCA Forest.
Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a "bias trick" that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.
A new robust scaling approach improves downstream metabolomics analysis.
This paper investigates differentially private analysis of distance-based outliers. The problem of outlier detection is to find a small number of instances that are apparently distant from the remaining instances. On the other hand, the objective of differential privacy is to conceal presence (or absence) of any partic…
Principal Component Analysis (PCA) is a popular tool for dimensionality reduction and feature extraction in data analysis. There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA). However, standard PCA and PPCA are not robust, as they are sensitive to outliers. To alleviate this problem, this paper i…
Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitti…
New method falsifies causal graphs using outlier events.
New robust MPCA method handles casewise and cellwise outliers in tensor data.
Integrates outlier detection into neural networks for improved performance.
A large dimensional characterization of robust M-estimators of covariance (or scatter) is provided under the assumption that the dataset comprises independent (essentially Gaussian) legitimate samples as well as arbitrary deterministic samples, referred to as outliers. Building upon recent random matrix advances in the…
Proposes AE for robust PCA, improving robustness to outliers.
Paper proposes a new method for selective inference in robust regression.
Robust TOT regression method handles outliers in tensor data.
Imaging genetic research has essentially focused on discovering unique and co-association effects, but typically ignoring to identify outliers or atypical objects in genetic as well as non-genetics variables. Identifying significant outliers is an essential and challenging issue for imaging genetics and multiple source…
New PCA method handles multiple datasets and detects sparse patterns robustly.
Paper uses genome Markov structure for outlier detection and read classification.
Study improves robustness and sparsity in linear regression with adversarial outliers and heavy-tailed noise.
Improved LDA with capped l_{2,1}-norm reduces outlier sensitivity.
WPCA improves subspace recovery robustness to outliers.
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…
RobPy offers robust statistical methods in Python.
A new meta-analysis model detects and accommodates outliers.
RieCUR improves Robust PCA by combining Riemannian optimization and CUR decompositions.
New method robustifies topological data analysis against outliers.
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…
Outliers arise in networks due to different reasons such as fraudulent behavior of malicious users or default in measurement instruments and can significantly impair network analyses. In addition, real-life networks are likely to be incompletely observed, with missing links due to individual non-response or machine fai…
Efficient SVD algorithm robust to outliers.
PyODDS automates outlier detection for new data sources.
New framework robustifies loss functions with quantiles for outlier resistance.
We describe a formal approach to identify 'root causes' of outliers observed in variables in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG). To this end, we first introduce a systematic way to define outlier scores. Further, we introduce the concep…
One-Class Boundary Peeling detects outliers efficiently and robustly.
New findings suggest deep generative models can misclassify outliers, requiring new evaluation methods.
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…
Modern machine learning-based recognition approaches require large-scale datasets with large number of labelled training images. However, such datasets are inherently difficult and costly to collect and annotate. Hence there is a great and growing interest in automatic dataset collection methods that can leverage the w…
A new robust Wasserstein distance is proposed to handle outliers in probability distributions.
This paper presents a remarkably simple, yet powerful, algorithm termed Coherence Pursuit (CoP) to robust Principal Component Analysis (PCA). As inliers lie in a low dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points. By contrast, outli…
PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without movi…
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…
Proposes MPCA for robust PCA using mode estimation.
Identifies root causes of outliers in unknown cyclic graphs.
Paper uses machine learning to forecast significant currency exchange rate fluctuations.
Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In this context, the fresh look advocated here permeates benefits from variable sele…
Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle…
Heavy-tailed distributions are frequently used to enhance the robustness of regression and classification methods to outliers in output space. Often, however, we are confronted with "outliers" in input space, which are isolated observations in sparsely populated regions. We show that heavy-tailed stochastic processes (…
SPCA improves PCA by learning from simple to complex samples.
A novel framework IMBoost improves outlier detection by leveraging the inlier memorization effect.
Cellwise outliers challenge traditional methods in statistics and machine learning.