52 citations · 194 across the 36 of their papers we have counts for
13 papers · 1 filter
Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers
Jun Ma, Xu Zhang, Zhengxing Jiao +4
Language-Assisted Image Clustering (LAIC) augments the input images with additional texts with the help of vision-language models (VLMs) to promote clustering performance. Despite…
DiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning
Bo Han, Zhuoming Li, Xiaoyu Wang +4
Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model's…
ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering
Xinyue Wang, Yuheng Jia, Hui Liu +1
Typical deep clustering methods, while achieving notable progress, can only provide one clustering result per dataset. This limitation arises from their assumption of a fixed under…
Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance
Jiecheng Jiang, Jiawei Tang, Jiahao Jiang +3
Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, whic…
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…
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…
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…
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Han Xu, Yaxin Li, Xiaorui Liu +2
Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training,…
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…