arXiv 2102.027912021-02-04cs.LGstat.ML

RECol: Reconstruction Error Columns for Outlier Detection

German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Deutsche Bundesbank（德意志联邦银行）

Jörn Hees, Dayananda Herurkar, Mario Meier

英文摘要：

Detecting outliers or anomalies is a common data analysis task. As a sub-field of unsupervised machine learning, a large variety of approaches exist, but the vast majority treats the input features as independent and often fails to recognize even simple (linear) relationships in the input feature space. Hence, we introduce RECol, a generic data pre-processing approach to generate additional columns in a leave-one-out-fashion: For each column, we try to predict its values based on the other columns, generating reconstruction error columns. We run experiments across a large variety of common baseline approaches and benchmark datasets with and without our RECol pre-processing method and show that the generated reconstruction error feature space generally seems to support common outlier detection methods and often considerably improves their ROC-AUC and PR-AUC values.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Fast, Parameter free Outlier Identification for Robust PCA

Indian Institute of Technology Madras（印度理工学院马德拉斯分校）

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 column sparse outlier model. The existing methods for column sparse outlier model assumes either the knowledge of the dimension of the lower dimensional subspace or the fraction of outliers in the system. However in many applications knowledge of these parameters is not available. Motivated by this we propose a parameter free outlier identification method for robust PCA which a) does not require the knowledge of outlier fraction, b) does not req…

2018-04-13cs.LGstat.ML

Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption

University of Central Florida（中佛罗里达大学）

We study a data model in which the data matrix D can be expressed as D = L + S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a matrix whose non-zero columns are outlying data points. To date, robust PCA algorithms have solely considered models with either S or C, but not both. As such, existing algorithms cannot account for simultaneous element-wise and column-wise corruptions. In this paper, a new robust PCA algorithm that is robust to simultaneous types of corruption is proposed. Our approach hinges on the sparse approximation of a sparsely corrupted column so that…

2017-02-07cs.CVcs.LGstat.ML

Structured and Unstructured Outlier Identification for Robust PCA: A Non iterative, Parameter free Algorithm

Indian Institute of Technology Madras（马德拉斯印度理工学院）

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 dimension of the lower dimensional subspace or the fraction of outliers in the system. However in many applications knowledge of these parameters is not available. Motivated by this we propose a parameter free outlier identification method for robust PCA which a) does not require th…

2018-09-11cs.LGstat.ML

基于单元的鲁棒双块降维

Cellwise Robust Twoblock Dimension Reduction

本文提出CRTB方法，首次实现对多变量预测器和响应块的同时降维，适用于密集和稀疏变量选择场景。通过列向量预过滤和迭代加权M估计循环，有效处理单元级异常值，保持部分污染行的清洁单元，提升鲁棒性和效率。

2026-04-16stat.ME

Robust Low-Complexity Randomized Methods for Locating Outliers in Large Matrices

This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing elements. We propose a randomized two-step inference framework, and establish sufficient conditions on the required sample complexities under which these methods succeed (with high probability) in accurately locating the outliers for each task. Comprehensive numerical experimental results are provided to verify the theoretical bounds and demonstrate the computational efficiency of the proposed algorithm.

2016-12-07cs.ITcs.LGmath.IT

多子空间矩阵从置换数据中的恢复

Multi-Subspace Matrix Recovery from Permuted Data

School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）

针对列受置换破坏的多子空间矩阵恢复问题，提出四阶段算法流程，含理论保证，在多个基准上优于现有方法。

2024-12-17cs.LGstat.ML

更多相关论文收起更多论文

Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling

University of Minnesota – Twin Cities（明尼苏达大学双城分校）

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance; our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix -- as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust coll…

2014-11-19cs.ITcs.LGmath.IT

计算分类学中的类信息学科学数据分析

Scientific Data Analysis for Class-Informatics in Computational Taxonomy

Federal University of Paraná（巴拉那联邦大学）；University of California at Davis（加州大学戴维斯分校）

提出计算分类学中的类信息学框架，通过科学数据分析范式探索关联模式，解决异常检测、自校正标注及数据信息内容相等性假设等基础问题，并在鸢尾花数据上展示。

2026-09-28stat.CO

鲁棒且可扩展的列/行采样从受腐蚀的大数据

Robust and Scalable Column/Row Sampling from Corrupted Big Data

University of Central Florida（中佛罗里达大学）

本文提出新的采样算法，能在严重数据腐蚀下定位信息列，并开发可扩展的随机化设计，同时对稀疏腐蚀和异常值具有鲁棒性，实验显示优于现有鲁棒采样算法。

2016-11-18cs.LGcs.NAmath.NA

Non-Exhaustive, Overlapping Co-Clustering: An Extended Analysis

Sungkyunkwan University（成均馆大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）

The goal of co-clustering is to simultaneously identify a clustering of rows as well as columns of a two dimensional data matrix. A number of co-clustering techniques have been proposed including information-theoretic co-clustering and the minimum sum-squared residue co-clustering method. However, most existing co-clustering algorithms are designed to find pairwise disjoint and exhaustive co-clusters while many real-world datasets contain not only a large overlap between co-clusters but also outliers which should not belong to any co-cluster. In this paper, we formulate the problem of Non-Exha…

2020-04-24cs.LGstat.ML

Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering

The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Cornell University（康奈尔大学）

Low dimensional nonlinear structure abounds in datasets across computer vision and machine learning. Kernelized matrix factorization techniques have recently been proposed to learn these nonlinear structures for denoising, classification, dictionary learning, and missing data imputation, by observing that the image of the matrix in a sufficiently large feature space is low-rank. However, these nonlinear methods fail in the presence of sparse noise or outliers. In this work, we propose a new robust nonlinear factorization method called Robust Non-Linear Matrix Factorization (RNLMF). RNLMF const…

2020-12-02cs.LGstat.ML

无标记主成分分析与矩阵补全

Unlabeled Principal Component Analysis and Matrix Completion

EPFL（洛桑联邦理工学院）

该研究提出列元素被置换破坏场景下的无标记主成分分析（UPCA），证明其为良定义代数问题，设计两阶段高效算法，并拓展至无标记矩阵补全，实验验证其在多领域应用潜力。

2023-10-09cs.LGstat.ML

ICS 的数值考量与新实现

Numerical considerations and a new implementation for ICS

本文针对 ICS 在病态条件下数值不稳定问题，提出基于中心化数据主元 QR 分解的新实现，避免直接计算散布矩阵及其逆，并支持非满秩情形，实验验证了其优势。

2022-07-05stat.CO

稳健的SVD变得简单：一种用于大规模数据分析的快速可靠算法

Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis

Seoul National University（首尔大学）

本文提出球面归一化SVD算法，通过两次标准降秩SVD实现快速且对异常值不敏感的稳健奇异向量近似，并引入矩阵崩溃点新概念，在高维微阵列数据中验证了其优于现有算法的稳健性与效率。

2024-02-15cs.LGmath.STstat.ML

Static and Dynamic Robust PCA and Matrix Completion: A Review

Iowa State University（爱荷华州立大学）

Principal Components Analysis (PCA) is one of the most widely used dimension reduction techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by outliers. Recent work by Cand{è}s, Wright, Li, and Ma defined RPCA as a problem of decomposing a given data matrix into the sum of a low-rank matrix (true data) and a sparse matrix (outliers). The column space of the low-rank matrix then gives the PCA solution. This simple definition has lead to a large amount of interesting new work on provably correct, fast, and practical solutions to RPCA. More recently, the dynam…

2018-05-25cs.ITcs.LGmath.IT

Bayesian Robust Tensor Factorization for Incomplete Multiway Data

RIKEN Brain Science Institute（理化学研究所脑科学研究所）；Shanghai Jiao Tong University（上海交通大学）

We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student…

2015-04-16cs.CVcs.LG