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Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive
  Learning to Identify Latent Subgroups in Political Parties
v1v2v3 (latest)

Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties

PLoS ONE (PLOS ONE), 2020
9 July 2020
Takanori Fujiwara
Tzu-Ping Liu
ArXiv (abs)PDFHTML

Papers citing "Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties"

3 / 3 papers shown
Visual Analytics Using Tensor Unified Linear Comparative Analysis
Visual Analytics Using Tensor Unified Linear Comparative Analysis
Naoki Okami
Kazuki Miyake
Naohisa Sakamoto
J. Nonaka
Takanori Fujiwara
224
2
0
26 Jul 2025
Interactive Dimensionality Reduction for Comparative Analysis
Interactive Dimensionality Reduction for Comparative AnalysisIEEE Transactions on Visualization and Computer Graphics (TVCG), 2021
Takanori Fujiwara
Xinhai Wei
Jian Zhao
K. Ma
190
43
0
29 Jun 2021
Network Comparison with Interpretable Contrastive Network Representation
  Learning
Network Comparison with Interpretable Contrastive Network Representation LearningJournal of Data Science Statistics and Visualisation (JDSSV), 2020
Takanori Fujiwara
Jian Zhao
Francine Chen
Yaoliang Yu
K. Ma
231
6
0
25 May 2020
1
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