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math.OC2022★ 1 cited
A Riemannian exponential augmented Lagrangian method for computing the projection robust Wasserstein distance
Bo Jiang, Ya-Feng Liu
Projecting the distance measures onto a low-dimensional space is an efficient way of mitigating the curse of dimensionality in the classical Wasserstein distance using optimal tran…
math.OC2022
An Efficient Alternating Riemannian/Projected Gradient Descent Ascent Algorithm for Fair Principal Component Analysis
Meng Xu, Bo Jiang, Wenqiang Pu +2
Fair principal component analysis (FPCA), a ubiquitous dimensionality reduction technique in signal processing and machine learning, aims to find a low-dimensional representation f…