Clustering Result Re-guided Incomplete Multi-view Spectral Clustering

• URL: http://arxiv.org/abs/2510.09959v1
• Date: Sat, 11 Oct 2025 02:15:29 GMT
• Title: Clustering Result Re-guided Incomplete Multi-view Spectral Clustering
• Authors: Jun Yin, Runcheng Cai, Shiliang Sun,
• Abstract summary: We propose Clustering Result re-Guided Incomplete Multi-view Spectral Clustering (CRG_IMSC)<n>CRG_IMSC obtains the clustering result directly by imposing nonnegative constraint to the extracted feature.<n>It constructs the connectivity matrix according to the result of spectral clustering, and minimizes the residual of self-representation.
• Score: 35.385466735523046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incomplete multi-view spectral clustering generalizes spectral clustering to multi-view data and simultaneously realizes the partition of multi-view data with missing views. For this category of method, K-means algorithm needs to be performed to generate the clustering result after the procedure of feature extraction. More importantly, the connectivity of samples reflected by the clustering result is not utilized effectively. To overcome these defects, we propose Clustering Result re-Guided Incomplete Multi-view Spectral Clustering (CRG_IMSC). CRG_IMSC obtains the clustering result directly by imposing nonnegative constraint to the extracted feature. Furthermore, it constructs the connectivity matrix according to the result of spectral clustering, and minimizes the residual of self-representation based on the connectivity matrix. A novel iterative algorithm using multiplicative update is developed to solve the optimization problem of CRG_IMSC, and its convergence is proved rigorously. On benchmark datasets, for multi-view data, CRG_IMSC performs better than state-of-the-art clustering methods, and the experimental results also demonstrate the convergence of CRG_IMSC algorithm.

Related papers

• Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data [0.29465623430708915]
  This paper presents a comprehensive analysis of prominent clustering algorithms K-means, DBSCAN, and Spectral Clustering on high-dimensional datasets.<n>We introduce a novel evaluation framework that assesses clustering performance across multiple dimensionality reduction techniques.
  arXiv  Detail & Related papers  (2025-03-29T20:38:04Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• One-Step Late Fusion Multi-view Clustering with Compressed Subspace [29.02032034647922]
  We propose an integrated framework named One-Step Late Fusion Multi-view Clustering with Compressed Subspace (OS-LFMVC-CS) We use the consensus subspace to align the partition matrix while optimizing the partition fusion, and utilize the fused partition matrix to guide the learning of discrete labels.
  arXiv  Detail & Related papers  (2024-01-03T06:18:30Z)
• Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [85.51611950757643]
  We propose IAC (Instance-Adaptive Clustering), the first algorithm whose performance matches the instance-specific lower bounds both in expectation and with high probability.<n>IAC maintains an overall computational complexity of $ mathcalO(n, textpolylog(n) $, making it scalable and practical for large-scale problems.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry [9.619814126465206]
  Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm. We show that incorporating spatial regularization into a multiscale clustering framework corresponds to smoother and more coherent clusters when applied to HSI data.
  arXiv  Detail & Related papers  (2021-03-29T17:24:28Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)
• 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)
• CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data [34.89460002735166]
  We study the problem of applying spectral clustering to cluster multi-scale data. For multi-scale data, distance-based similarity is not effective because objects of a sparse cluster could be far apart. We propose the algorithm CAST that applies trace Lasso to regularize the coefficient matrix.
  arXiv  Detail & Related papers  (2020-06-08T09:46:35Z)
• Non-Exhaustive, Overlapping Co-Clustering: An Extended Analysis [32.15852903039789]
  The goal of co-clustering is to simultaneously identify a clustering of rows as well as columns of a two dimensional data matrix. We develop an efficient iterative algorithm which we call the NEO-CC algorithm. Experimental results show that the NEO-CC algorithm is able to effectively capture the underlying co-clustering structure of real-world data.
  arXiv  Detail & Related papers  (2020-04-24T04:39:14Z)

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.