82 citations · 123 across the 20 of their papers we have counts for
20 papers
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…
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…
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
Rong Wang, Yihang Lu, Qianrong Zhang +3
In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields. As a classic HAD method, the collabo…
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…
Low Rank Regularization: A Review
Zhanxuan Hu, Feiping Nie, Rong Wang +1
Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many field…