3 citations · 3 across the 2 of their papers we have counts for
3 papers
math.OC2022
No Dimension-Free Deterministic Algorithm Computes Approximate Stationarities of Lipschitzians
Lai Tian, Anthony Man-Cho So
We consider the computation of an approximately stationary point for a Lipschitz and semialgebraic function with a local oracle. If is smooth, simple deterministic methods…
cs.LG2019★ 3 cited
Learning Feature Sparse Principal Components
Lai Tian, Feiping Nie, Xuelong Li
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization met…
cs.LG2016
Non-Greedy L21-Norm Maximization for Principal Component Analysis
Feiping Nie, Heng Huang
Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen…