Advances in Data Analysis and Classification最新文献_第8页

Flexible mixture regression with the generalized hyperbolic distribution 使用广义双曲分布的灵活混合回归

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-01-04 DOI: 10.1007/s11634-022-00532-4

Nam-Hwui Kim, Ryan P. Browne

引用次数: 0

Sparse correspondence analysis for large contingency tables 大型列联表的稀疏对应分析

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2023-01-02 DOI: 10.1007/s11634-022-00531-5

Ruiping Liu, Ndeye Niang, Gilbert Saporta, Huiwen Wang

引用次数: 2

Proximal methods for sparse optimal scoring and discriminant analysis 稀疏最优评分和判别分析的近似方法

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-12-21 DOI: 10.1007/s11634-022-00530-6

Summer Atkins, Gudmundur Einarsson, Line Clemmensen, Brendan Ames

{"title":"Proximal methods for sparse optimal scoring and discriminant analysis","authors":"Summer Atkins, Gudmundur Einarsson, Line Clemmensen, Brendan Ames","doi":"10.1007/s11634-022-00530-6","DOIUrl":"10.1007/s11634-022-00530-6","url":null,"abstract":"<div><p>Linear discriminant analysis (LDA) is a classical method for dimensionality reduction, where discriminant vectors are sought to project data to a lower dimensional space for optimal separability of classes. Several recent papers have outlined strategies, based on exploiting sparsity of the discriminant vectors, for performing LDA in the high-dimensional setting where the number of features exceeds the number of observations in the data. However, many of these proposed methods lack scalable methods for solution of the underlying optimization problems. We consider an optimization scheme for solving the sparse optimal scoring formulation of LDA based on block coordinate descent. Each iteration of this algorithm requires an update of a scoring vector, which admits an analytic formula, and an update of the corresponding discriminant vector, which requires solution of a convex subproblem; we will propose several variants of this algorithm where the proximal gradient method or the alternating direction method of multipliers is used to solve this subproblem. We show that the per-iteration cost of these methods scales linearly in the dimension of the data provided restricted regularization terms are employed, and cubically in the dimension of the data in the worst case. Furthermore, we establish that when this block coordinate descent framework generates convergent subsequences of iterates, then these subsequences converge to the stationary points of the sparse optimal scoring problem. We demonstrate the effectiveness of our new methods with empirical results for classification of Gaussian data and data sets drawn from benchmarking repositories, including time-series and multispectral X-ray data, and provide <span>Matlab</span> and <span>R</span> implementations of our optimization schemes.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"17 4","pages":"983 - 1036"},"PeriodicalIF":1.6,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50502301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

LASSO regularization within the LocalGLMnet architecture LocalGLMnet体系结构中的LASSO正则化

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-12-13 DOI: 10.1007/s11634-022-00529-z

Ronald Richman, Mario V. Wüthrich

引用次数: 0

A power-controlled reliability assessment for multi-class probabilistic classifiers 多类概率分类器的功率控制可靠性评估

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-11-17 DOI: 10.1007/s11634-022-00528-0

Hyukjun Gweon

引用次数: 1

A dual subspace parsimonious mixture of matrix normal distributions 矩阵正态分布的对偶子空间简约混合

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-11-16 DOI: 10.1007/s11634-022-00526-2

Alex Sharp, Glen Chalatov, Ryan P. Browne

引用次数: 1

Monitoring photochemical pollutants based on symbolic interval-valued data analysis 基于符号区间值数据分析的光化学污染物监测

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-11-12 DOI: 10.1007/s11634-022-00527-1

Liang-Ching Lin, Meihui Guo, Sangyeol Lee

{"title":"Monitoring photochemical pollutants based on symbolic interval-valued data analysis","authors":"Liang-Ching Lin, Meihui Guo, Sangyeol Lee","doi":"10.1007/s11634-022-00527-1","DOIUrl":"10.1007/s11634-022-00527-1","url":null,"abstract":"<div><p>This study considers monitoring photochemical pollutants for anomaly detection based on symbolic interval-valued data analysis. For this task, we construct control charts based on the principal component scores of symbolic interval-valued data. Herein, the symbolic interval-valued data are assumed to follow a normal distribution, and an approximate expectation formula of order statistics from the normal distribution is used in the univariate case to estimate the mean and variance via the method of moments. In addition, we consider the bivariate case wherein we use the maximum likelihood estimator calculated from the likelihood function derived under a bivariate copula. We also establish the procedures for the statistical control chart based on the univariate and bivariate interval-valued variables, and the procedures are potentially extendable to higher dimensional cases. Monte Carlo simulations and real data analysis using photochemical pollutants confirm the validity of the proposed method. The results particularly show the superiority over the conventional method that uses the averages to identify the date on which the abnormal maximum occurred.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"17 4","pages":"897 - 926"},"PeriodicalIF":1.6,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50045936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial for ADAC issue 4 of volume 16 (2022) ADAC第16卷第4期社论（2022）

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-10-31 DOI: 10.1007/s11634-022-00525-3

Maurizio Vichi, Andrea Ceroli, Hans A. Kestler, Akinori Okada, Claus Weihs

引用次数: 0

Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering 吸引-排斥聚类:一种在公平聚类中促进与人口均等相关的多样性的方法

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-10-20 DOI: 10.1007/s11634-022-00516-4

Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes

{"title":"Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering","authors":"Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes","doi":"10.1007/s11634-022-00516-4","DOIUrl":"10.1007/s11634-022-00516-4","url":null,"abstract":"<div><p>We consider the problem of <i>diversity enhancing clustering</i>, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the context of <i>fair clustering</i>, diversity plays a major role when fairness is understood as demographic parity. To promote diversity, we introduce perturbations to the distance in the unprotected attributes that account for protected attributes in a way that resembles attraction-repulsion of charged particles in Physics. These perturbations are defined through dissimilarities with a tractable interpretation. Cluster analysis based on attraction-repulsion dissimilarities penalizes homogeneity of the clusters with respect to the protected attributes and leads to an improvement in diversity. An advantage of our approach, which falls into a pre-processing set-up, is its compatibility with a wide variety of clustering methods and whit non-Euclidean data. We illustrate the use of our procedures with both synthetic and real data and provide discussion about the relation between diversity, fairness, and cluster structure.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"17 4","pages":"859 - 896"},"PeriodicalIF":1.6,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11634-022-00516-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50040006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A structured covariance ensemble for sufficient dimension reduction 一种用于充分降维的结构化协方差系综

IF 1.6 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2022-10-19 DOI: 10.1007/s11634-022-00524-4

Qin Wang, Yuan Xue

引用次数: 1