C Zhang, X Chen, S Ma - Mathematics of Operations …, 2024 - pubsonline.informs.org
In this paper, we study the generalized subdifferentials and the Riemannian gradient subconsistency that are the basis for non-Lipschitz optimization on embedded submanifolds …
This paper is devoted to studying an augmented Lagrangian method for solving a class of manifold optimization problems, which have nonsmooth objective functions and nonlinear …
This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We …
M Huang, S Ma, L Lai - IEEE Transactions on Signal …, 2021 - ieeexplore.ieee.org
Robust low-rank matrix completion (RMC), or robust principal component analysis with partially observed data, has been studied extensively for computer vision, signal processing …
Y Xu, A Srinivasan, L Xue - Modern Statistical Methods for Health …, 2021 - Springer
Clustering is a key technique in segmenting data into different groups of similar observations. As clustering is an unsupervised learning method, the latent cluster …
B Wang, S Ma, L Xue - Journal of machine learning research, 2022 - jmlr.org
Riemannian optimization has drawn a lot of attention due to its wide applications in practice. Riemannian stochastic first-order algorithms have been studied in the literature to solve …
Spectral clustering is one of the fundamental unsupervised learning methods and is widely used in data analysis. Sparse spectral clustering (SSC) imposes sparsity to the spectral …
We consider optimization problems over the Stiefel manifold whose objective function is the summation of a smooth function and a nonsmooth function. Existing methods for solving this …
2,1 norm minimization with orthogonality constraints, which comprise a feasible region called the Stiefel manifold, has wide applications in statistics and data science. The state-of …