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pro vyhledávání: '"CHAVENT, Marie"'
This paper introduces a methodology based on Denoising AutoEncoder (DAE) for missing data imputation. The proposed methodology, called mDAE hereafter, results from a modification of the loss function and a straightforward procedure for choosing the h
Externí odkaz:
http://arxiv.org/abs/2411.12847
Autor:
Chavent, Marie, Chavent, Guy
Block Principal Component Analysis (Block PCA) of a data matrix A, where loadings Z are determined by maximization of AZ 2 over unit norm orthogonal loadings, is difficult to use for the design of sparse PCA by 1 regularization, due to the difficulty
Externí odkaz:
http://arxiv.org/abs/2402.04692
Akademický článek
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In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. The dissimilarities can be
Externí odkaz:
http://arxiv.org/abs/1707.03897
Autor:
Chavent, Marie, Chavent, Guy
We address the problem of defining a group sparse formulation for Principal Components Analysis (PCA) - or its equivalent formulations as Low Rank approximation or Dictionary Learning problems - which achieves a compromise between maximizing the vari
Externí odkaz:
http://arxiv.org/abs/1705.00461
Standard approaches to tackle high-dimensional supervised classification problem often include variable selection and dimension reduction procedures. The novel methodology proposed in this paper combines clustering of variables and feature selection.
Externí odkaz:
http://arxiv.org/abs/1608.06740
Akademický článek
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Mixed data arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends to this type of data standard multivariate analysis methods which allow description, exploration and visualization
Externí odkaz:
http://arxiv.org/abs/1411.4911
Autor:
Chavent, Marie1 marie.chavent@u-bordeaux.fr, Kuentz, Vanessa2, Labenne, Amaury2, Saracco, Jérôme1
Publikováno v:
Electronic Journal of Applied Statistical Analysis. Nov2022, Vol. 15 Issue 3, p606-645. 40p.
Autor:
Chavent, Marie, Saracco, Jérôme
Publikováno v:
Communications in Statistics - Theory and Methods 37, 9 (2008) 1471 - 1482
The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued
Externí odkaz:
http://arxiv.org/abs/0804.2247