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Re-embedding data to strengthen recovery guarantees of clustering

Re-embedding data to strengthen recovery guarantees of clustering

26 January 2023
Tao Jiang
Samuel Tan
S. Vavasis
    FedML
ArXiv (abs)PDFHTML

Papers citing "Re-embedding data to strengthen recovery guarantees of clustering"

8 / 8 papers shown
Title
Local versions of sum-of-norms clustering
Local versions of sum-of-norms clustering
Alexander Dunlap
J. Mourrat
51
4
0
20 Sep 2021
On Convex Clustering Solutions
On Convex Clustering Solutions
Canh Hao Nguyen
Hiroshi Mamitsuka
37
6
0
18 May 2021
Sum-of-norms clustering does not separate nearby balls
Sum-of-norms clustering does not separate nearby balls
Alexander Dunlap
J. Mourrat
35
3
0
28 Apr 2021
Recovery of a mixture of Gaussians by sum-of-norms clustering
Recovery of a mixture of Gaussians by sum-of-norms clustering
Tao Jiang
S. Vavasis
C. Zhai
36
18
0
19 Feb 2019
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Defeng Sun
Kim-Chuan Toh
Yancheng Yuan
60
68
0
04 Oct 2018
Statistical Properties of Convex Clustering
Statistical Properties of Convex Clustering
Kean Ming Tan
Daniela Witten
70
89
0
28 Mar 2015
Fast tree inference with weighted fusion penalties
Fast tree inference with weighted fusion penalties
J. Chiquet
P. Gutierrez
G. Rigaill
78
26
0
22 Jul 2014
Splitting Methods for Convex Clustering
Splitting Methods for Convex Clustering
Eric C. Chi
K. Lange
107
266
0
01 Apr 2013
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