Robust Multi-view Clustering against Imperfect Information

• URL: http://arxiv.org/abs/2606.04343v1
• Date: Wed, 03 Jun 2026 01:55:41 GMT
• Title: Robust Multi-view Clustering against Imperfect Information
• Abstract summary: Posterior-guided Latent Counterpart Inference (PLCI) is a novel framework for tackling the imperfect information problem.<n>PLCI formulates the desired cross-view counterpart of each anchor instance as a latent variable.
• Score: 22.44765600975703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (i.e., Incomplete Views, IV) and cross-view correspondences are mismatched (i.e., Noisy Correspondences, NC) for certain instances. As a remedy, numerous IV- and NC-oriented multi-view clustering (MvC) methods have been proposed, which however require either reliable correspondences or sufficiently complete instances, thus stopping short of addressing the imperfect information problem. In contrast, we observe that both IV and NC challenges originate from the same issue of imperfect cross-view counterpart information, where the counterpart of an anchor instance in another view might be either unavailable or unreliable. Based on the observation, we propose a novel robust MvC framework, termed Posterior-guided Latent Counterpart Inference (PLCI), which could handle both IV and NC in a unified manner. Specifically, PLCI formulates the desired cross-view counterpart of each anchor instance as a latent variable, and integrates both instance-level reliability and prototype-level semantic transport to infer the posterior distribution of the latent counterpart. Extensive experiments on six widely-used multi-view datasets against 10 state-of-the-art MvC methods demonstrate the effectiveness of PLCI for tackling the imperfect information problem. The code will be released upon acceptance.

Related papers

• Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence [72.41989962665285]
  Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views.<n>However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views.<n>We design a causal multi-view clustering network, termed CauMVC, to tackle this problem.
  arXiv  Detail & Related papers  (2025-09-19T14:31:40Z)
• 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.<n>We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views.<n>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)
• Evidential Deep Partial Multi-View Classification With Discount Fusion [24.139495744683128]
  We propose a novel framework called Evidential Deep Partial Multi-View Classification (EDP-MVC) We use K-means imputation to address missing views, creating a complete set of multi-view data. The potential conflicts and uncertainties within this imputed data can affect the reliability of downstream inferences.
  arXiv  Detail & Related papers  (2024-08-23T14:50:49Z)
• Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding [7.305817202715752]
  We propose a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC) Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information.
  arXiv  Detail & Related papers  (2023-12-14T07:28:41Z)
• Scalable Incomplete Multi-View Clustering with Structure Alignment [71.62781659121092]
  In this paper, we propose a novel incomplete anchor graph learning framework. We construct the view-specific anchor graph to capture the complementary information from different views. The time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples.
  arXiv  Detail & Related papers  (2023-08-31T08:30:26Z)
• 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)
• Semantic Invariant Multi-view Clustering with Fully Incomplete Information [28.321343045226772]
  We present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy. Our approach improves the clustering accuracy of NoisyMNIST from 19.3%/23.2% to 82.7%/69.0% when the correspondences/instances are fully incomplete.
  arXiv  Detail & Related papers  (2023-05-22T06:11:01Z)
• 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)
• 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.