The Guedon-Vershynin Semi-definite Programming Approach to Low Dimensional Embedding for Unsupervised Clustering

This paper proposes a method for estimating the cluster matrix in the Gaussian mixture framework via Semi-Definite Programming. Theoretical error bounds are provided and a (non linear) low dimensional embedding of the data is deduced from the cluster matrix estimate. The method and its analysis is i...

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Detalles Bibliográficos

Principais autores:	Stéphane Chrétien, Clément Dombry, Adrien Faivre
Formato:	Artigo
Idioma:	Inglês
Publicado:	Frontiers Media S.A. 2019-10-01
Series:	Frontiers in Applied Mathematics and Statistics
Assuntos:	semi-definite program (SDP) clustering (un-supervised) algorithms Gaussian mixture (GM) model embedding convex relation
Acceso en liña:	https://www.frontiersin.org/article/10.3389/fams.2019.00041/full
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