Balanced Multi-view Clustering
- URL: http://arxiv.org/abs/2501.02564v3
- Date: Tue, 04 Feb 2025 11:01:02 GMT
- Title: Balanced Multi-view Clustering
- Authors: Zhenglai Li, Jun Wang, Chang Tang, Xinzhong Zhu, Wei Zhang, Xinwang Liu,
- Abstract summary: Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures.
The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information.
We propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view.
- Score: 56.17836963920012
- License:
- Abstract: Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets.
Related papers
- An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization [0.0]
Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data.
Existing MVC methods struggle with effectively quantifying the consistency and complementarity among views.
We propose CE-MVC, a novel framework that integrates an adaptive weighting algorithm with a parameter-decoupled deep model.
arXiv Detail & Related papers (2024-12-23T15:21:55Z) - Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment [13.511433241138702]
Partial multi-view clustering (PVC) presents significant challenges practical research problem for data analysis in real-world applications.
Existing clustering methods struggle to handle incomplete views effectively, leading to suboptimal clustering performance.
We propose a novel dual optimization framework based on contrastive learning, which aims to maximize the consistency of latent features in incomplete multi-view data.
arXiv Detail & Related papers (2024-11-14T19:16:01Z) - A Novel Approach for Effective Multi-View Clustering with
Information-Theoretic Perspective [24.630259061774836]
This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint.
Firstly, we develop a simple and reliable multi-view clustering method SCMVC that employs variational analysis to generate consistent information.
Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views.
arXiv Detail & Related papers (2023-09-25T09:41:11Z) - DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC)
We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
arXiv Detail & Related papers (2023-08-17T14:14:28Z) - Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and
Prototype Alignment [50.82982601256481]
We propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering.
Unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction.
arXiv Detail & Related papers (2023-03-28T02:31:57Z) - Self-supervised Discriminative Feature Learning for Multi-view
Clustering [12.725701189049403]
We propose self-supervised discriminative feature learning for multi-view clustering (SDMVC)
Concretely, deep autoencoders are applied to learn embedded features for each view independently.
Experiments on various types of multi-view datasets show that SDMVC achieves state-of-the-art performance.
arXiv Detail & Related papers (2021-03-28T07:18:39Z) - A Variational Information Bottleneck Approach to Multi-Omics Data
Integration [98.6475134630792]
We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations.
Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target.
Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
arXiv Detail & Related papers (2021-02-05T06:05:39Z) - Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from
Cross View and Each View [68.88732535086338]
This paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization.
Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.
arXiv Detail & Related papers (2020-08-23T08:25:06Z) - Generative Partial Multi-View Clustering [133.36721417531734]
We propose a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem.
First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views.
Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views.
arXiv Detail & Related papers (2020-03-29T17:48:27Z)
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.