4 papers · 1 filter
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…
Robust Capped lp-Norm Support Vector Ordinal Regression
Haorui Xiang, Zhichang Wu, Guoxu Li +3
Ordinal regression is a specialized supervised problem where the labels show an inherent order. The order distinguishes it from normal multi-class problem. Support Vector Ordinal R…