6 papers
Multi-class Support Vector Machine with Maximizing Minimum Margin
Zhezheng Hao, Feiping Nie, Rong Wang
Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximi…
Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework
Guanxiong He, Jie Wang, Liaoyuan Tang +3
Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data. However, it is hampered by the flaw that current approaches are for…
Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy
Guanzhou Lan, Yuqi Yang, Anup Teejo Mathew +5
Goal-conditioned dynamic manipulation is inherently challenging due to complex system dynamics and stringent task constraints, particularly in deformable object scenarios character…
A Greedy Strategy for Graph Cut
Feiping Nie, Shenfei Pei, Zengwei Zheng +2
We propose a Greedy strategy to solve the problem of Graph Cut, called GGC. It starts from the state where each data sample is regarded as a cluster and dynamically merges the two…
Fast Semi-supervised Learning on Large Graphs: An Improved Green-function Method
Feiping Nie, Yitao Song, Wei Chang +2
In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space. However, when applied…
Clustering Based on Density Propagation and Subcluster Merging
Feiping Nie, Yitao Song, Jingjing Xue +2
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unl…