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New ORCA method enhances multi-view clustering accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ORCA (Orthogonal Residual Consensus Alignment), a novel method for deep multi-view clustering designed to mitigate issues with redundant information and indiscriminate alignment. ORCA employs a semantic incremental recurrent fusion module to extract non-redundant, cross-view orthogonal residuals and a consensus-calibrated hierarchical contrastive alignment module that leverages global consensus to weight alignment across different representation levels. Experiments on five benchmarks showed ORCA's superiority, achieving a 5.25 percent accuracy increase over the leading baseline on the 100leaves dataset. AI

IMPACT Introduces a novel method to improve the accuracy and efficiency of multi-view clustering tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for multi-view clustering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New ORCA method enhances multi-view clustering accuracy

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The cluster contains an academic paper detailing a new method for multi-view clustering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaojian Ding, Xian Li, Xiaoying Zhu, Lin Zhao, Kaixiang Wang ·

    ORCA:Orthogonal Residual Consensus Alignment for Multi-View Clustering

    arXiv:2611.16749v1 Announce Type: cross Abstract: Deep multi-view clustering often suffers from redundant information accumulation during fusion and indiscriminate alignment across representation levels. We propose Orthogonal Residual Consensus Alignment (ORCA) to address these c…

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