2 citations · 3 across the 4 of their papers we have counts for
4 papers
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Jinghui Yuan, Fangyuan Xie, Feiping Nie +1
The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this r…
Double-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clusterin
Fangyuan Xie, Jinghui Yuan, Feiping Nie +1
Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple so…
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Jinghui Yuan, Weijin Jiang, Zhe Cao +4
Ensemble learning is a method that leverages weak learners to produce a strong learner. However, obtaining a large number of base learners requires substantial time and computation…
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Jinghui Yuan, Chusheng Zeng, Fangyuan Xie +5
Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain. Doubly stochastic symmetri…