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graph learning

Graph learning is a machine learning paradigm that focuses on automatically constructing, optimizing, and analyzing graph structures to represent complex relationships and pairwise similarities among data points [1, 2]. Rather than relying solely on predefined network topologies, graph learning algorithms infer the underlying connectivity, adjacency weights, and latent geometric properties directly from raw or multi-view data [1, 3]. This process enables models to capture intrinsic structural dependencies, filter out noise, and adaptively establish meaningful connections across datasets [1, 2]. The resulting learned graphs and node representations are widely utilized to enhance performance in downstream analytical tasks, including subspace and spectral clustering, node classification, community detection, and dimensionality reduction [1, 2].

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Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

Suyuan Liu, Siwei Wang, Pei Zhang, Kai Xu, Xinwang Liu, Changwang Zhang, Feng Gao

OrganizationsChina Computer FederationNational University of Defense TechnologyPeking University

Why you should read this

Proposes a scalable multi-view subspace clustering method that jointly learns consensus anchors and a fused graph with exact connected components, achieving linear time complexity and directly generating cluster labels without heuristic anchor sampling or post-processing steps.

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the sub-sequential anchor graph construction, which may adversely affect the clustering performance. In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we combine anchor learning and graph construction into a uniform framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. Our proposed EOMSC-CA is proven to be linear complexity respecting to the data size. The superiority of our EOMSC-CA over the effectiveness and efficiency is demonstrated by extensive experiments. Our code is publicly available at https://github.com/Tracesource/EOMSC-CA.

Added

2026-09-26