An Efficient Alternating Riemannian/Projected Gradient Descent Ascent Algorithm for Fair Principal Component Analysis-Reference-Cited by-同舟云学术

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An Efficient Alternating Riemannian/Projected Gradient Descent Ascent Algorithm for Fair Principal Component Analysis

Published:2024-04-14 Issue: Volume:31 Page:7195-7199
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Xu Meng¹,Jiang Bo²,Pu Wenqiang³,Liu Ya-Feng¹,So Anthony Man-Cho⁴

Affiliation:

1. LSEC, ICMSEC, AMSS, Chinese Academy of Sciences,Beijing,China

2. Nanjing Normal University,Ministry of Education Key Laboratory of NSLSCS, School of Mathematical Sciences,Nanjing,China

3. The Chinese University of Hong Kong,Shenzhen Research Institute of Big Data,Shenzhen,China

4. The Chinese University of Hong Kong,Department of Sys. Eng. & Eng. Mgmt,HKSAR,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447172.pdf?arnumber=10447172

Reference30 articles.

1. The price of fair PCA: One extra dimension;Samadi

2. Multi-criteria dimensionality reduction with applications to fairness;Tantipongpipat

3. Fair Principal Component Analysis and Filter Design

4. Efficient fair principal component analysis

5. Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold

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