Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

• URL: http://arxiv.org/abs/2503.14024v1
• Date: Tue, 18 Mar 2025 08:35:39 GMT
• Title: Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
• Authors: Pingting Hao, Kunpeng Liu, Wanfu Gao,
• Abstract summary: We propose a unified model constructed from the perspective of global-view reconstruction.<n>We incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness.<n> Experimental results demonstrate the superior performance of our method on multi-view datasets.
• Score: 4.176139684578661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets.

Related papers

• Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance [69.58609684008964]
  We propose BURG, a novel method for incomplete multi-view clustering with distriBution dUal-consistency Recovery Guidance. We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views. To compensate for the lack of reliable category information, we design a dual-consistency guided recovery strategy that includes intra-view alignment guided by neighbor-aware consistency and cross-view alignment guided by prototypical consistency.
  arXiv  Detail & Related papers  (2025-03-14T02:27:45Z)
• Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation [61.64052577026623]
  Real-world multi-view datasets are often heterogeneous and imperfect.<n>We propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment.<n>In experiments, we employ it in unsupervised multi-view clustering, noise-label classification, and as a plug-and-play module for cross-modal hashing retrieval.
  arXiv  Detail & Related papers  (2025-03-06T07:01:08Z)
• Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification [9.905528765058541]
  We propose a novel framework for incomplete multi-view multi-label classification (iMvMLC)<n>Our method factorizes multi-view representations into two independent sets of factors: view-consistent and view-specific.<n>Our framework innovatively decomposes consistent representation learning into three key sub-objectives.
  arXiv  Detail & Related papers  (2025-01-11T12:19:20Z)
• Rethinking Multi-view Representation Learning via Distilled Disentangling [34.14711778177439]
  Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain, highlighting the redundancy between view-consistent and view-specific representations. We propose an innovative framework for multi-view representation learning, which incorporates a technique we term 'distilled disentangling'
  arXiv  Detail & Related papers  (2024-03-16T11:21:24Z)
• 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)
• A Clustering-guided Contrastive Fusion for Multi-view Representation Learning [7.630965478083513]
  We propose a deep fusion network to fuse view-specific representations into the view-common representation. We also design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation. In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors.
  arXiv  Detail & Related papers  (2022-12-28T07:21:05Z)
• MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion [4.36488705757229]
  Multi-view representation learning is essential for many multi-view tasks, such as clustering and classification. We propose a hybrid contrastive fusion algorithm to extract robust view-common representation from unlabeled data. Experimental results demonstrated that the proposed method outperforms 12 competitive multi-view methods on four real-world datasets.
  arXiv  Detail & Related papers  (2022-08-26T09:58:37Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)
• Deep Partial Multi-View Learning [94.39367390062831]
  We propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets) We fifirst provide a formal defifinition of completeness and versatility for multi-view representation. We then theoretically prove the versatility of the learned latent representations.
  arXiv  Detail & Related papers  (2020-11-12T02:29:29Z)
• Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation [11.91574721055601]
  This paper proposes a novel multi-view learning method, named Multi-view Low-rank Preserving Embedding (MvLPE) It integrates different views into one centroid view by minimizing the disagreement term, based on distance or similarity matrix among instances. Experiments on six benchmark datasets demonstrate that the proposed method outperforms its counterparts.
  arXiv  Detail & Related papers  (2020-06-14T12:47:25Z)
• 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.