52 citations · 168 across the 28 of their papers we have counts for
17 papers · 1 filter
Two-Point Local Optimality in -Means via Boundary-Point Screening
Wenlong Lyu, Xujie Xiao, Yuheng Jia
Lloyd's algorithm and the discrete local (D-local) optimization method (Li et al., 2025) for -means provide only weak local-optimality guarantees, and their solution quality rem…
Graph-based Clustering Revisited: A Relaxation of Kernel -Means Perspective
Wenlong Lyu, Yuheng Jia, Hui Liu +1
The well-known graph-based clustering methods, including spectral clustering, symmetric non-negative matrix factorization, and doubly stochastic normalization, can be viewed as rel…
Generalization Performance of Ensemble Clustering: From Theory to Algorithm
Xu Zhang, Haoye Qiu, Weixuan Liang +3
Ensemble clustering has demonstrated great success in practice; however, its theoretical foundations remain underexplored. This paper examines the generalization performance of ens…
Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
Fuchao Yang, Jianhong Cheng, Hui Liu +3
In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the…
Exploiting Multi-Label Correlation in Label Distribution Learning
Zhiqiang Kou jing wang yuheng jia xin geng
Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in…
Complementary Classifier Induced Partial Label Learning
Yuheng Jia, Chongjie Si, Min-ling Zhang
In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate…
Data Augmentation For Label Enhancement
Zhiqiang Kou, Yuheng Jia, Jing Wang +2
Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless…
Label Distribution Learning from Logical Label
Yuheng Jia, Jiawei Tang, Jiahao Jiang
Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. However, annotating label distribution (LD…
Inaccurate Label Distribution Learning
Zhiqiang Kou, Yuheng Jia, Jing Wang +1
Label distribution learning (LDL) trains a model to predict the relevance of a set of labels (called label distribution (LD)) to an instance. The previous LDL methods all assumed t…
EGRC-Net: Embedding-induced Graph Refinement Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can lead to failures when the initial graph fails to accurately capture the data topology str…
A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery
Yang He, Yuheng Jia, Liyang Hu +3
Traffic data chronically suffer from missing and corruption, leading to accuracy and utility reduction in subsequent Intelligent Transportation System (ITS) applications. Noticing…
Multi-label Classification with High-rank and High-order Label Correlations
Chongjie Si, Yuheng Jia, Ran Wang +3
Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a la…
Ensemble Clustering via Co-association Matrix Self-enhancement
Yuheng Jia, Sirui Tao, Ran Wang +1
Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many tim…
Self-supervised Symmetric Nonnegative Matrix Factorization
Yuheng Jia, Hui Liu, Junhui Hou +2
Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimizat…
Clustering Ensemble Meets Low-rank Tensor Approximation
Yuheng Jia, Hui Liu, Junhui Hou +1
This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. The existing…
Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation
Yuheng Jia, Hui Liu, Junhui Hou +2
This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-r…
Clustering-aware Graph Construction: A Joint Learning Perspective
Yuheng Jia, Hui Liu, Junhui Hou +1
Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent…