Towards Learnable Anchor for Deep Multi-View Clustering

• URL: http://arxiv.org/abs/2503.12427v1
• Date: Sun, 16 Mar 2025 09:38:11 GMT
• Title: Towards Learnable Anchor for Deep Multi-View Clustering
• Authors: Bocheng Wang, Chusheng Zeng, Mulin Chen, Xuelong Li,
• Abstract summary: In this paper, we propose the Deep Multi-view Anchor Clustering (DMAC) model that performs clustering in linear time.<n>With the optimal anchors, the full sample graph is calculated to derive a discriminative embedding for clustering.<n>Experiments on several datasets demonstrate superior performance and efficiency of DMAC compared to state-of-the-art competitors.
• Score: 49.767879678193005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep multi-view clustering incorporating graph learning has presented tremendous potential. Most methods encounter costly square time consumption w.r.t. data size. Theoretically, anchor-based graph learning can alleviate this limitation, but related deep models mainly rely on manual discretization approaches to select anchors, which indicates that 1) the anchors are fixed during model training and 2) they may deviate from the true cluster distribution. Consequently, the unreliable anchors may corrupt clustering results. In this paper, we propose the Deep Multi-view Anchor Clustering (DMAC) model that performs clustering in linear time. Concretely, the initial anchors are intervened by the positive-incentive noise sampled from Gaussian distribution, such that they can be optimized with a newly designed anchor learning loss, which promotes a clear relationship between samples and anchors. Afterwards, anchor graph convolution is devised to model the cluster structure formed by the anchors, and the mutual information maximization loss is built to provide cross-view clustering guidance. In this way, the learned anchors can better represent clusters. With the optimal anchors, the full sample graph is calculated to derive a discriminative embedding for clustering. Extensive experiments on several datasets demonstrate the superior performance and efficiency of DMAC compared to state-of-the-art competitors.

Related papers

• Anchor Learning with Potential Cluster Constraints for Multi-view Clustering [11.536710289572552]
  Anchor-based multi-view clustering (MVC) has received extensive attention due to its efficient performance.<n>We propose a noval method termed Anchor Learning with Potential Cluster Constraints for Multi-view Clustering (ALPC) method.
  arXiv  Detail & Related papers  (2024-12-21T07:43:05Z)
• Discriminative Anchor Learning for Efficient Multi-view Clustering [59.11406089896875]
  We propose discriminative anchor learning for multi-view clustering (DALMC) We learn discriminative view-specific feature representations according to the original dataset. We build anchors from different views based on these representations, which increase the quality of the shared anchor graph.
  arXiv  Detail & Related papers  (2024-09-25T13:11:17Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number [12.926206811876174]
  We introduce a novel self-supervised deep clustering approach tailored for unstructured data, termed Adaptive Self-supervised Robust Clustering (ASRC) ASRC adaptively learns the graph structure and edge weights to capture both local and global structural information. ASRC even outperforms methods that rely on prior knowledge of the number of clusters, highlighting its effectiveness in addressing the challenges of clustering unstructured data.
  arXiv  Detail & Related papers  (2024-07-29T15:51:09Z)
• Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization [64.00146569922028]
  Multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix. We address this limitation by using non-negative tensor factorization to decompose an anchor graph tensor that combines anchor graphs from multiple views.
  arXiv  Detail & Related papers  (2024-04-01T03:23:55Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• End-to-end Learnable Clustering for Intent Learning in Recommendation [54.157784572994316]
  We propose a novel intent learning method termed underlineELCRec. It unifies behavior representation learning into an underlineEnd-to-end underlineLearnable underlineClustering framework. We deploy this method on the industrial recommendation system with 130 million page views and achieve promising results.
  arXiv  Detail & Related papers  (2024-01-11T15:22:55Z)
• A Deep Dive into Deep Cluster [0.2578242050187029]
  DeepCluster is a simple and scalable unsupervised pretraining of visual representations. We show that DeepCluster convergence and performance depend on the interplay between the quality of the randomly filters of the convolutional layer and the selected number of clusters.
  arXiv  Detail & Related papers  (2022-07-24T22:55:09Z)
• Self-Evolutionary Clustering [1.662966122370634]
  Most existing deep clustering methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping. A novel modular Self-Evolutionary Clustering (Self-EvoC) framework is constructed, which boosts the clustering performance by classification in a self-supervised manner. The framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of self-supervised.
  arXiv  Detail & Related papers  (2022-02-21T19:38:18Z)
• Learning Statistical Representation with Joint Deep Embedded Clustering [2.1267423178232407]
  StatDEC is an unsupervised framework for joint statistical representation learning and clustering. Our experiments show that using these representations, one can considerably improve results on imbalanced image clustering across a variety of image datasets.
  arXiv  Detail & Related papers  (2021-09-11T09:26:52Z)
• Learning to Cluster Faces via Confidence and Connectivity Estimation [136.5291151775236]
  We propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs. Our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods.
  arXiv  Detail & Related papers  (2020-04-01T13:39:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.