52 citations · 168 across the 28 of their papers we have counts for
29 papers
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…
Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
Jianhan Qi, Yuheng Jia, Hui Liu +1
Hyperspectral image (HSI) clustering groups pixels into clusters without labeled data, which is an important yet challenging task. For large-scale HSIs, most methods rely on superp…
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…
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
Yang He, Chengchuan An, Yuheng Jia +3
Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose…
Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
Bo Han, Yuheng Jia, Hui Liu +1
Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank tensor representation has emerged as a robust strategy, leveraging in…
Towards Calibrated Deep Clustering Network
Yuheng Jia, Jianhong Cheng, Hui Liu +1
Deep clustering has exhibited remarkable performance; however, the over confidence problem, i.e., the estimated confidence for a sample belonging to a particular cluster greatly ex…
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images
Jianhan Qi, Yuheng Jia, Hui Liu +1
Hyperspectral images (HSI) clustering is an important but challenging task. The state-of-the-art (SOTA) methods usually rely on superpixels, however, they do not fully utilize 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…
Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
Yuheng Jia, Guanxing Lu, Hui Liu +1
In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discrimin…
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…
Deep Attention-guided Graph Clustering with Dual Self-supervision
Zhihao Peng, Hui Liu, Yuheng Jia +1
Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cl…
Adaptive Attribute and Structure Subspace Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
Deep self-expressiveness-based subspace clustering methods have demonstrated effectiveness. However, existing works only consider the attribute information to conduct the self-expr…
Attention-driven Graph Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-e…
Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification
Shujun Yang, Junhui Hou, Yuheng Jia +2
In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics,…
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…