Advances in Data Analysis and Classification最新文献_第5页

Functional independent component analysis by choice of norm: a framework for near-perfect classification 通过规范选择的功能独立成分分析：近乎完美分类的框架

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-01-30 DOI: 10.1007/s11634-024-00622-5

Marc Vidal, Marc Leman, Ana M. Aguilera

{"title":"Functional independent component analysis by choice of norm: a framework for near-perfect classification","authors":"Marc Vidal, Marc Leman, Ana M. Aguilera","doi":"10.1007/s11634-024-00622-5","DOIUrl":"10.1007/s11634-024-00622-5","url":null,"abstract":"<div><p>We develop a theory for functional independent component analysis in an infinite-dimensional framework using Sobolev spaces that accommodate smoother functions. The notion of penalized kurtosis is introduced motivated by Silverman’s method for smoothing principal components. This approach allows for a classical definition of independent components obtained via projection onto the eigenfunctions of a smoothed kurtosis operator mapping a whitened functional random variable. We discuss the theoretical properties of this operator in relation to a generalized Fisher discriminant function and the relationship it entails with the Feldman-Hájek dichotomy for Gaussian measures, both of which are critical to the principles of functional classification. The proposed estimators are a particularly competitive alternative in binary classification of functional data and can eventually achieve the so-called near-perfect classification, which is a genuine phenomenon of high-dimensional data. Our methods are illustrated through simulations, various real datasets, and used to model electroencephalographic biomarkers for the diagnosis of depressive disorder.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 2","pages":"521 - 549"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314746","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

Kernel biclustering algorithm in Hilbert spaces Hilbert空间中的核双聚类算法

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-01-21 DOI: 10.1007/s11634-024-00619-0

Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Dino Sejdinovic

{"title":"Kernel biclustering algorithm in Hilbert spaces","authors":"Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Dino Sejdinovic","doi":"10.1007/s11634-024-00619-0","DOIUrl":"10.1007/s11634-024-00619-0","url":null,"abstract":"<div><p>Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions. This paper develops a new model-free biclustering algorithm in abstract spaces using the notions of energy distance and the maximum mean discrepancy—two distances between probability distributions capable of handling complex data such as curves or graphs. The proposed method can learn more general and complex cluster shapes than most existing literature approaches, which usually focus on detecting mean and variance differences. Although the biclustering configurations of our approach are constrained to create disjoint structures at the datum and covariate levels, the results are competitive. Our results are similar to state-of-the-art methods in their optimal scenarios, assuming a proper kernel choice, outperforming them when cluster differences are concentrated in higher-order moments. The model’s performance has been tested in several situations that involve simulated and real-world datasets. Finally, new theoretical consistency results are established using some tools of the optimal transport theory.\u0000</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"20 2","pages":"479 - 520"},"PeriodicalIF":2.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314718","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

Enhancing symbolic regression with side information for data analysis 增强符号回归与侧信息的数据分析

IF 2.1 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2025-01-09 DOI: 10.1007/s11634-024-00621-6

Xiangwu Zuo, Anxiao Jiang

引用次数: 0

Clustering and classification of spatio-temporal data using spatial dynamic panel data models 利用空间动态面板数据模型对时空数据进行聚类和分类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-12-23 DOI: 10.1007/s11634-024-00620-7

Giuseppe Feo, Francesco Giordano, Sara Milito, Marcella Niglio, Maria Lucia Parrella

引用次数: 0

Directional density-based clustering 基于定向密度的聚类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-12-11 DOI: 10.1007/s11634-024-00616-3

Paula Saavedra-Nieves, Martín Fernández-Pérez

引用次数: 0

Correction: Characterisation and calibration of multiversal methods 校正：通用方法的表征和校准

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-12-10 DOI: 10.1007/s11634-024-00618-1

Giulio Giacomo Cantone, Venera Tomaselli

引用次数: 0

Classification of multivariate count data with multivariate log-linear conditional Poisson distribution 多元对数线性条件泊松分布的多元计数数据分类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-12-07 DOI: 10.1007/s11634-024-00617-2

Juan M. Muñoz-Pichardo, Rafael Pino-Mejías

引用次数: 0

Reducing the dimensionality and granularity in hierarchical categorical variables 降低层次分类变量的维数和粒度

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-11-24 DOI: 10.1007/s11634-024-00614-5

Paul Wilsens, Katrien Antonio, Gerda Claeskens

{"title":"Reducing the dimensionality and granularity in hierarchical categorical variables","authors":"Paul Wilsens, Katrien Antonio, Gerda Claeskens","doi":"10.1007/s11634-024-00614-5","DOIUrl":"10.1007/s11634-024-00614-5","url":null,"abstract":"<div><p>Hierarchical categorical variables often exhibit many levels (high granularity) and many classes within each level (high dimensionality). This may cause overfitting and estimation issues when including such covariates in a predictive model. In current literature, a hierarchical covariate is often incorporated via nested random effects. However, this does not facilitate the assumption of classes having the same effect on the response variable. In this paper, we propose a methodology to obtain a reduced representation of a hierarchical categorical variable. We show how entity embedding can be applied in a hierarchical setting. Subsequently, we propose a top-down clustering algorithm which leverages the information encoded in the embeddings to reduce both the within-level dimensionality as well as the overall granularity of the hierarchical categorical variable. In simulation experiments, we show that our methodology can effectively approximate the true underlying structure of a hierarchical covariate in terms of the effect on a response variable, and find that incorporating the reduced hierarchy improves the balance between model fit and complexity. We apply our methodology to a real dataset and find that the reduced hierarchy is an improvement over the original hierarchical structure and reduced structures proposed in the literature.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"1087 - 1118"},"PeriodicalIF":1.3,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612765","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

Unsupervised curve clustering using wavelets 基于小波的无监督曲线聚类

IF 1.3 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-11-16 DOI: 10.1007/s11634-024-00612-7

Umberto Amato, Anestis Antoniadis, Italia De Feis, Irène Gijbels

{"title":"Unsupervised curve clustering using wavelets","authors":"Umberto Amato, Anestis Antoniadis, Italia De Feis, Irène Gijbels","doi":"10.1007/s11634-024-00612-7","DOIUrl":"10.1007/s11634-024-00612-7","url":null,"abstract":"<div><p>Clustering univariate functional data is mostly based on projecting the curves onto an adequate basis and applying some distance or similarity models on the coefficients. The basis functions should be chosen depending on features of the function being estimated. Commonly used are Fourier, polynomial and splines, but these may not be well suited for curves that exhibit inhomogeneous behavior. Wavelets on the contrary are well suited for identifying highly discriminant local time and scale features, and are able to adapt to the data smoothness. In recent years, few methods, relying on wavelet-based similarity measures, have been proposed for clustering curves, observed on equidistant points. In this work, we present a non-equidistant design wavelet based method for non-parametrically estimating and clustering a large number of curves. The method consists of several crucial stages: fitting functional data by non-equispaced design wavelet regression, screening out nearly flat curves, denoising the remaining curves with wavelet thresholding, and finally clustering the denoised curves. Simulation studies compare our proposed method with some other functional clustering methods. The method is applied for clustering some real functional data profiles.</p></div>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"19 4","pages":"1051 - 1085"},"PeriodicalIF":1.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612712","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 18 (2024) 为 ADAC 第 18 卷第 4 期（2024 年）撰写的社论

IF 1.4 4区计算机科学

Advances in Data Analysis and Classification Pub Date : 2024-11-15 DOI: 10.1007/s11634-024-00615-4

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

引用次数: 0