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optimal anchor graph

An optimal anchor graph is a bipartite graph representation used in machine learning and clustering that models relationships between high-dimensional data points and a smaller set of representative landmark points, known as anchors, through a unified optimization process. Unlike conventional anchor graphs that rely on predetermined or fixed anchor points separated from graph construction, an optimal anchor graph is obtained by jointly learning anchor positions, edge weights, and task-specific structural constraints such as graph connectivity or subspace properties. This simultaneous optimization enables the graph to accurately reflect the underlying geometric distributions and cluster structures of the data while maintaining linear computational scalability with respect to sample size, facilitating efficient data partitioning and representation learning across large-scale and multi-view domains.

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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