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Unified and Efficient Multi-view Clustering from Probabilistic Perspective

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Multi-view clusteringanchorefficiencya unified manner
Abstract:

Multi-view clustering aims to segment the view-specific data into the corresponding clusters. There have been a large number of works for multi-view clustering in recent years. As representive methods in multi-view clustering, works built on the graph make use of a view-consistent and discriminative graph while utilizing graph partitioning for the final clustering results. Despite the achieved significant success, these methods usually construct full graphs and the efficiency is not well guaranteed for the multi-view datasets with large scales. To handle the large-scale data, multi-view clustering methods based on anchor have been developed by learning the anchor graph with smaller size. However, the existing works neglect the interpretability of multi-view clustering based on anchor from the probabilistic perspective. These methods also ignore analyzing the relationship between the input data and the final clustering results based on the assigned meaningful probability associations in a unified manner. In this work, we propose a novel method termed Unified and Efficient Multi-view Clustering from Probabilistic perspective(UEMCP). It aims to improve the explanation ability of multi-view clustering based on anchor from the probabilistic perspective in an end-to-end manner. It ensures the consistent inherent structures among these views by learning the common transition probability from data points to categories in one step. With the guidance of the common transition probability matrix from data points to categories, the soft label of data points can be achieved based on the common transition probability matrix from anchor points to categories in the learning framework. Experiments on different challenging multi-view datasets confirm the superiority of UEMCP compared with the representative ones.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes UEMCP, a unified probabilistic framework for anchor-based multi-view clustering that aims to improve interpretability by establishing explicit probabilistic connections between data and cluster assignments. It resides in the 'Direct Transition Probability Learning' leaf of the taxonomy, which contains only two papers total. This leaf sits within the 'Unified Probabilistic Framework Methods' branch, indicating the paper addresses a relatively focused research direction rather than a densely populated area. The small number of sibling papers suggests this specific approach to unified probabilistic modeling remains underexplored compared to other multi-view clustering paradigms.

The taxonomy reveals that anchor-based multi-view clustering research divides into three main branches: unified probabilistic frameworks, anchor enhancement techniques, and incomplete data handling. The paper's leaf neighbors a 'Tensor-Based Probability Aggregation' approach, suggesting alternative strategies for combining view-specific information exist nearby. The broader 'Anchor Enhancement and Alignment Methods' branch addresses complementary concerns like cross-view anchor correspondence and adaptive subspace learning, which the paper does not emphasize. The taxonomy structure indicates the field balances trade-offs between probabilistic expressiveness, computational efficiency, and robustness to data irregularities, with this work prioritizing the first two aspects.

Among 20 candidates examined, all three core contributions show evidence of prior work overlap. The unified probabilistic framework contribution examined 6 candidates with 3 appearing refutable, while common transition probability learning examined 10 candidates with 6 showing overlap. The soft label derivation mechanism examined 4 candidates with 2 refutable. These statistics suggest that within the limited search scope, each contribution encounters substantial related work, though the analysis does not claim exhaustive coverage. The relatively high proportion of refutable candidates across contributions indicates the core ideas have precedents in the examined literature, though the specific integration and formulation may differ.

Based on the top-20 semantic matches examined, the work appears to build on established concepts in anchor-based probabilistic clustering, with multiple contributions showing overlap with prior methods. The taxonomy placement in a sparsely populated leaf suggests the specific unified formulation may offer a distinct perspective, but the contribution-level analysis reveals that individual technical components have been explored previously. The limited search scope means additional relevant work may exist beyond the candidates examined, particularly in adjacent research areas not fully captured by semantic similarity.

Taxonomy

Core-task Taxonomy Papers
5
3
Claimed Contributions
20
Contribution Candidate Papers Compared
11
Refutable Paper

Research Landscape Overview

Core task: multi-view clustering with anchor-based probabilistic transition matrices. This field addresses the challenge of integrating information from multiple data views while maintaining computational efficiency through anchor-based representations. The taxonomy reveals three main branches that capture distinct methodological emphases. Unified Probabilistic Framework Methods focus on learning transition probabilities that connect data points to anchors in a principled probabilistic manner, often optimizing joint objectives that balance clustering quality and cross-view consistency. Anchor Enhancement and Alignment Methods concentrate on refining anchor selection and ensuring that anchors from different views correspond meaningfully, addressing issues such as anchor shift and cross-view alignment to improve the reliability of the learned representations. Incomplete Multi-View Clustering with Anchors tackles scenarios where some views are partially observed, developing strategies to handle missing data while still leveraging anchor-based efficiency. Representative works such as Transition Probability Clustering[3] and Alleviate Anchor Shift[2] illustrate how these branches operationalize their respective priorities. Several active lines of work highlight key trade-offs in the field. One central tension involves balancing the expressiveness of probabilistic models against the computational savings that anchors provide, with some methods pursuing more flexible transition structures while others prioritize scalability through simpler anchor schemes. Another recurring theme concerns the robustness of anchor-based representations when views are misaligned or incomplete, prompting research into adaptive anchor selection and alignment mechanisms as seen in Scalable Anchor Alignment[1] and Adaptive Anchor Subspace[5]. The original paper, Unified Probabilistic Multiview[0], sits within the Direct Transition Probability Learning cluster of the Unified Probabilistic Framework branch. Compared to Transition Probability Clustering[3], which also emphasizes direct learning of transition probabilities, Unified Probabilistic Multiview[0] appears to pursue a more integrated probabilistic treatment that unifies multiple views under a single coherent framework, rather than treating each view's transitions independently before fusion.

Claimed Contributions

Unified and Efficient Multi-view Clustering from Probabilistic perspective (UEMCP)

The authors introduce UEMCP, a new multi-view clustering method that assigns probabilistic interpretations to anchor graphs and soft labels. This approach enhances the interpretability of anchor-based multi-view clustering by providing meaningful probability associations between input data and clustering results in a unified framework.

6 retrieved papers
Can Refute
Common transition probability learning across views

The method learns a shared transition probability matrix from data points to categories across all views in a single step. This ensures that the inherent data structures remain consistent across different views, addressing the challenge of maintaining view consistency in multi-view clustering.

10 retrieved papers
Can Refute
Soft label derivation via anchor-to-category transition probabilities

The authors develop a mechanism to derive soft labels for data points by introducing a transition probability matrix from anchor points to categories. This is guided by the common transition probability from data points to categories, enabling a unified learning procedure that connects anchors, data points, and final cluster assignments.

4 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unified and Efficient Multi-view Clustering from Probabilistic perspective (UEMCP)

The authors introduce UEMCP, a new multi-view clustering method that assigns probabilistic interpretations to anchor graphs and soft labels. This approach enhances the interpretability of anchor-based multi-view clustering by providing meaningful probability associations between input data and clustering results in a unified framework.

Contribution

Common transition probability learning across views

The method learns a shared transition probability matrix from data points to categories across all views in a single step. This ensures that the inherent data structures remain consistent across different views, addressing the challenge of maintaining view consistency in multi-view clustering.

Contribution

Soft label derivation via anchor-to-category transition probabilities

The authors develop a mechanism to derive soft labels for data points by introducing a transition probability matrix from anchor points to categories. This is guided by the common transition probability from data points to categories, enabling a unified learning procedure that connects anchors, data points, and final cluster assignments.

Table of Contents