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Explainable KK-means Neural Networks for Multi-view Clustering

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Multi-view clusteringefficiencyeffectivenesscompleteness and consistency
Abstract:

Despite multi-view clustering has achieved great progress in past decades, it is still a challenge to balance the effectiveness, efficiency, completeness and consistency of nonlinearly separable clustering for the data from different views. To address this challenge, we show that multi-view clustering can be regarded as a three-level optimization problem. To be specific, we divide the multi-view clustering into three sub-problems based on KK-means or kernel KK-means, i.e., linear clustering on the original multi-view dataset, nonlinear clustering on the set of obtained linear clusters and multi-view clustering by integrating partition matrices from different views obtained by linear and nonlinear clustering based on reconstruction. We propose Explainable KK-means Neural Networks (EKNN) and present how to unify these three sub-problems into a framework based on EKNN. It is able to simultaneously consider the effectiveness, efficiency, completeness and consistency for the nonlinearly multi-view clustering and can be optimized by an iterative algorithm. EKNN is explainable since the effect of each layer is known. To the best of our knowledge, this is the first attempt to balance the effectiveness, efficiency, completeness and consistency by dividing the multi-view clustering into three different sub-problems. Extensive experimental results demonstrate the effectiveness and efficiency of EKNN compared with other methods for multi-view clustering on different datasets in terms of different metrics.

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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 a three-level optimization framework for multi-view clustering that decomposes the problem into linear clustering, nonlinear clustering on linear clusters, and multi-view integration via reconstruction. It introduces Explainable K-means Neural Networks (EKNN) to unify these stages. Within the taxonomy, this work resides in the 'Unified Multi-Level Optimization Frameworks' leaf, which contains only four papers total. This is one of the smallest branches in the taxonomy, suggesting a relatively sparse research direction compared to crowded areas like deep autoencoder methods or kernel subspace clustering.

The taxonomy reveals that most multi-view clustering research concentrates in kernel-based methods, deep learning approaches, and tensor decomposition—each containing multiple subcategories with five or more papers. The paper's leaf sits apart from these mainstream directions, sharing conceptual boundaries with subspace learning and correlation methods but distinguished by its explicit multi-level formulation. Neighboring branches like 'Subspace Learning with Nonlinear Manifold Alignment' and 'Matrix Factorization' address related geometry-preserving goals, yet none frame the problem as hierarchical optimization stages integrating linear and nonlinear clustering explicitly.

Among thirty candidates examined, the three-level optimization formulation and EKNN framework show no clear refutation across twenty candidates reviewed. However, the extension to multi-view subspace learning encountered two refutable candidates among ten examined, indicating some overlap with existing subspace methods. The limited search scope means these statistics reflect top-thirty semantic matches rather than exhaustive coverage. The core contributions appear more distinctive than the subspace extension, which aligns with established work in manifold-based clustering.

Given the sparse population of the 'Unified Multi-Level Optimization Frameworks' leaf and the absence of refutation for the core formulation among examined candidates, the work appears to occupy a less-explored niche. The analysis covers top-thirty semantic neighbors and does not claim exhaustive field coverage. The subspace extension shows more prior overlap, suggesting incremental refinement in that aspect, while the three-level decomposition and EKNN architecture represent less-charted territory within the examined scope.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: multi-view clustering with nonlinearly separable data. The field addresses the challenge of grouping samples drawn from multiple heterogeneous views when linear separability assumptions fail. The taxonomy reveals a rich landscape organized into eight main branches. Kernel-Based Nonlinear Transformation Methods (e.g., Kernelized Auto-weighted Graph[3], Low-rank Kernel Subspace[4]) map data into higher-dimensional feature spaces to expose cluster structure. Deep Learning-Based Nonlinear Representation Methods (e.g., Deep Nonnegative Tensor[1], Deep Majorization[9]) leverage neural architectures to learn complex embeddings. Tensor-Based Multi-View Clustering (e.g., Hyper-Laplacian Tensor[15], Anchor Graph Tensor[50]) exploits higher-order structures to capture cross-view dependencies. Subspace Learning with Nonlinear Manifold Alignment (e.g., Aligning Nonlinear Manifolds[5]) seeks low-dimensional embeddings that respect intrinsic geometry. Correlation and Similarity Learning Methods (e.g., Canonical Correlation Clustering[12], Elastic High-order Correlations[7]) focus on measuring and fusing view-specific relationships. Specialized Clustering Frameworks address domain-specific constraints, Matrix Factorization and Decomposition Methods provide interpretable factorizations, and Unified Multi-Level Optimization Frameworks integrate multiple objectives into joint optimization schemes. Recent work shows a tension between interpretability and expressiveness. Deep methods achieve strong empirical performance but often lack transparency, while kernel and tensor approaches offer more structured representations at the cost of scalability. Within the Unified Multi-Level Optimization Frameworks branch, Explainable K-means Neural[0] emphasizes interpretability by combining neural clustering with explainable mechanisms, contrasting with purely performance-driven deep models like Deep Adversarial Clustering[26]. Its neighbors Multi-view-means[18] and Manifold K-Means[43] similarly prioritize geometric clarity and algorithmic transparency over black-box deep architectures. This positioning reflects a broader trend: as the field matures, researchers increasingly seek methods that balance nonlinear modeling power with understandable decision boundaries, especially for applications requiring trust and insight into cluster formation.

Claimed Contributions

Three-level optimization formulation for multi-view clustering

The authors formulate multi-view clustering as three sub-problems: linear clustering on original data points using K-means, nonlinear clustering on linear clusters using kernel K-means, and multi-view clustering by integrating partition matrices from different views. This formulation aims to balance effectiveness, efficiency, completeness, and consistency.

10 retrieved papers
Explainable K-means Neural Networks (EKNN) framework

The authors propose EKNN, a unified framework that integrates the three sub-problems of multi-view clustering. The framework is explainable because the effect of each layer is known, with layers corresponding to linear clustering, nonlinear clustering, and multi-view integration. An iterative algorithm is used for optimization.

10 retrieved papers
Extension of EKNN to multi-view subspace learning

The authors extend EKNN to learn a shared latent representation across views using self-expressiveness. This extension enables the framework to obtain subspace representations that can be used with existing clustering algorithms like spectral clustering.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Three-level optimization formulation for multi-view clustering

The authors formulate multi-view clustering as three sub-problems: linear clustering on original data points using K-means, nonlinear clustering on linear clusters using kernel K-means, and multi-view clustering by integrating partition matrices from different views. This formulation aims to balance effectiveness, efficiency, completeness, and consistency.

Contribution

Explainable K-means Neural Networks (EKNN) framework

The authors propose EKNN, a unified framework that integrates the three sub-problems of multi-view clustering. The framework is explainable because the effect of each layer is known, with layers corresponding to linear clustering, nonlinear clustering, and multi-view integration. An iterative algorithm is used for optimization.

Contribution

Extension of EKNN to multi-view subspace learning

The authors extend EKNN to learn a shared latent representation across views using self-expressiveness. This extension enables the framework to obtain subspace representations that can be used with existing clustering algorithms like spectral clustering.

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