Zobrazeno 1 - 10
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pro vyhledávání: '"Pelletier, Bruno"'
One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data. Sometimes referred to as manifold learning, this problem is closely related
Externí odkaz:
http://arxiv.org/abs/1810.09569
We are given the adjacency matrix of a geometric graph and the task of recovering the latent positions. We study one of the most popular approaches which consists in using the graph distances and derive error bounds under various assumptions on the l
Externí odkaz:
http://arxiv.org/abs/1804.10611
Despite a substantial literature on nonparametric two-sample goodness-of-fit testing in arbitrary dimensions spanning decades, there is no mention there of any curse of dimensionality. Only more recently Ramdas et al. (2015) have discussed this issue
Externí odkaz:
http://arxiv.org/abs/1607.08156
Autor:
Arias-Castro, Ery, Pelletier, Bruno
Rosenbaum (2005) proposed the crossmatch test for two-sample goodness-of-fit testing in arbitrary dimensions. We prove that the test is consistent against all fixed alternatives. In the process, we develop a general consistency result based on (Henze
Externí odkaz:
http://arxiv.org/abs/1509.05790
Autor:
Arias-Castro, Ery, Pelletier, Bruno
Maximum Variance Unfolding is one of the main methods for (nonlinear) dimensionality reduction. We study its large sample limit, providing specific rates of convergence under standard assumptions. We find that it is consistent when the underlying sub
Externí odkaz:
http://arxiv.org/abs/1209.0016
Publikováno v:
Advances in Applied Probability (2012) Volume 44, Number 4, 907-937
Let M be a bounded domain of a Euclidian space with smooth boundary. We relate the Cheeger constant of M and the conductance of a neighborhood graph defined on a random sample from M. By restricting the minimization defining the latter over a particu
Externí odkaz:
http://arxiv.org/abs/1004.5485
Autor:
Pelletier, Bruno, Pudlo, Pierre
Following Hartigan, a cluster is defined as a connected component of the t-level set of the underlying density, i.e., the set of points for which the density is greater than t. A clustering algorithm which combines a density estimate with spectral cl
Externí odkaz:
http://arxiv.org/abs/1002.2313
Autor:
Arias-Castro, Ery, Pelletier, Bruno
Publikováno v:
In Journal of Statistical Planning and Inference April 2016 171:184-190
Autor:
Frouin, Robert, Pelletier, Bruno
Publikováno v:
In Remote Sensing of Environment 15 March 2015 159:332-360
Publikováno v:
Advances in Applied Probability, 2012 Dec 01. 44(4), 907-937.
Externí odkaz:
https://www.jstor.org/stable/41805984