7 results match your criteria Advances in Data Analysis and Classification[Journal]

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Modal clustering of matrix-variate data.

Adv Data Anal Classif 2022 May 5:1-23. Epub 2022 May 5.

Dipartimento di Scienze Statistiche, Università degli Studi di Padova, Padua, Italy.

The nonparametric formulation of density-based clustering, known as modal clustering, draws a correspondence between groups and the attraction domains of the modes of the density function underlying the data. Its probabilistic foundation allows for a natural, yet not trivial, generalization of the approach to the matrix-valued setting, increasingly widespread, for example, in longitudinal and multivariate spatio-temporal studies. In this work we introduce nonparametric estimators of matrix-variate distributions based on kernel methods, and analyze their asymptotic properties. Read More

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Basis expansion approaches for functional analysis of variance with repeated measures.

Adv Data Anal Classif 2022 Apr 9:1-31. Epub 2022 Apr 9.

Department of Statistics and Operations Research and IMAG, University of Granada, Granada, Spain.

The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several independent samples of subjects walk or run under different conditions (repeated measures). Classic kinematic studies often analyse discrete summaries of the sample curves discarding important information and providing biased results. Read More

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Unobserved classes and extra variables in high-dimensional discriminant analysis.

Adv Data Anal Classif 2022 1;16(1):55-92. Epub 2022 Mar 1.

Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France.

In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions. Read More

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From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering.

Adv Data Anal Classif 2019 24;13(1):33-64. Epub 2018 Aug 24.

Institute for Statistics and Mathematics, Vienna University of Economics and Business (WU), Welthandelsplatz 1, 1020 Vienna, Austria.

In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303-324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with components is chosen in such a way that a priori the number of clusters in the data is random and is allowed to be smaller than with high probability. Read More

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Ensemble of a subset of NN classifiers.

Adv Data Anal Classif 2018 22;12(4):827-840. Epub 2016 Jan 22.

1Department of Mathematical Sciences, University of Essex, Colchester, CO4 3SQ UK.

Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of NN classifiers, ESNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. Read More

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January 2016

Improved initialisation of model-based clustering using Gaussian hierarchical partitions.

Adv Data Anal Classif 2015 Dec 26;9(4):447-460. Epub 2015 Oct 26.

Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195-4322.

Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood function and, so to different clustering partitions. Among the several approaches available in the literature, model-based agglomerative hierarchical clustering is used to provide initial partitions in the popular mclust R package. Read More

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December 2015

Assessing and accounting for time heterogeneity in stochastic actor oriented models.

Adv Data Anal Classif 2011 Jul;5(2):147-176

Department of Statistics, University of Oxford, Oxford, UK. Network Science Center, United States Military Academy, New York, USA.

This paper explores time heterogeneity in stochastic actor oriented models (SAOM) proposed by Snijders (Sociological Methodology. Blackwell, Boston, pp 361-395, 2001) which are meant to study the evolution of networks. SAOMs model social networks as directed graphs with nodes representing people, organizations, etc. Read More

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