Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric-Reference-Cited by-同舟云学术

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Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Zeng Pengxin¹,Li Yunfan¹,Hu Peng¹,Peng Dezhong¹,Lv Jiancheng¹,Peng Xi¹

Affiliation:

1. College of Computer Science, Sichuan University,China

Funder

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203404.pdf?arnumber=10203404

Reference46 articles.

1. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

2. From Ensemble Clustering to Multi-View Clustering

3. Empirical risk min-imization under fairness constraints;donini;ADVANCES IN NEURAL IN-FORMATION PROCESSING SYSTEMS,2018

4. Compressed K-Means for Large-Scale Clustering

5. Mapping the Mouse Cell Atlas by Microwell-Seq

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1. Deep fair clustering with multi-level decorrelation;Information Sciences;2024-10

2. A dual Laplacian framework with effective graph learning for unified fair spectral clustering;Neurocomputing;2024-10

3. A survey on deep clustering: from the prior perspective;Vicinagearth;2024-06-25

4. Fairness First Clustering: A Multi-Stage Approach for Mitigating Bias;Electronics;2023-07-05

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