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

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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Bibliographic Details
Main Authors: Stéphane Chrétien, Clément Dombry, Adrien Faivre
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Applied Mathematics and Statistics
Subjects:
semi-definite program (SDP)
clustering (un-supervised) algorithms
Gaussian mixture (GM) model
embedding
convex relation
Online Access:https://www.frontiersin.org/article/10.3389/fams.2019.00041/full

Internet

https://www.frontiersin.org/article/10.3389/fams.2019.00041/full

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