PECOK: a convex optimization approach to variable clustering

16 June 2016

Papers citing "PECOK: a convex optimization approach to variable clustering"

9 / 9 papers shown

Title
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous DataInternational Conference on Machine Learning (ICML), 2022 Yubo Zhuang Xiaohui Chen Yun Yang 138 1 0 29 Sep 2022
Diffusion $K$ -means clustering on manifolds: provable exact recovery via semidefinite relaxations Xiaohui Chen Yun Yang 323 17 0 11 Mar 2019
Hanson-Wright inequality in Hilbert spaces with application to $K$ -means clustering for non-Euclidean data Xiaohui Chen Yun Yang 204 34 0 26 Oct 2018
Partial recovery bounds for clustering with the relaxed $K$ meansMathematical Statistics and Learning (MSL), 2018 Christophe Giraud Nicolas Verzélen 418 62 0 19 Jul 2018
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models Carson Eisenach Han Liu 86 4 0 01 Jun 2018
Quantization/clustering: when and why does k-means work? Clément Levrard MQ 110 21 0 11 Jan 2018
Clustering with feature selection using alternating minimization, Application to computational biology Cyprien Gilet M. Deprez Jean-Baptiste Caillau Michel Barlaud 166 3 0 08 Nov 2017
Adaptive Clustering through Semidefinite Programming Martin Royer 175 31 0 18 May 2017
Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering Xin Bing F. Bunea Y. Ning M. Wegkamp 435 40 0 23 Apr 2017