82 citations · 123 across the 20 of their papers we have counts for
17 papers · 1 filter
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…
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…
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance
Chusheng Zeng, Bocheng Wang, Jinghui Yuan +2
Recent advances in unsupervised deep graph clustering have been significantly promoted by contrastive learning. Despite the strides, most graph contrastive learning models face cha…
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…
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…
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…
A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization
Feiping Nie, Jitao Lu, Danyang Wu +2
Normalized-Cut (N-Cut) is a famous model of spectral clustering. The traditional N-Cut solvers are two-stage: 1) calculating the continuous spectral embedding of normalized Laplaci…
Joint Feature and Differentiable -NN Graph Learning using Dirichlet Energy
Lei Xu, Lei Chen, Rong Wang +2
Feature selection (FS) plays an important role in machine learning, which extracts important features and accelerates the learning process. In this paper, we propose a deep FS meth…
Exploring the cloud of feature interaction scores in a Rashomon set
Sichao Li, Rong Wang, Quanling Deng +1
Interactions among features are central to understanding the behavior of machine learning models. Recent research has made significant strides in detecting and quantifying feature…
On the Global Solution of Soft k-Means
Feiping Nie, Hong Chen, Rong Wang +1
This paper presents an algorithm to solve the Soft k-Means problem globally. Unlike Fuzzy c-Means, Soft k-Means (SkM) has a matrix factorization-type objective and has been shown t…
New Tight Relaxations of Rank Minimization for Multi-Task Learning
Wei Chang, Feiping Nie, Rong Wang +1
Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace. It means learning multiple tasks joi…
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
Feiping Nie, Zhanxuan Hu, Xiaoqian Wang +3
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace…
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…
Feature Learning Viewpoint of AdaBoost and a New Algorithm
Fei Wang, Zhongheng Li, Fang He +3
The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the mysteries of this phenomena is a very fascinating fundamental theoretical problem. Many studi…