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Consensus-aware sparse subspace clustering via coreset selection
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DOI：10.1016/j.patcog.2026.113097.png)
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Abstract
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• We propose a scalable self-expressive model based on consensus learning across multiple subsets to address the connectivity issue in the affinity matrix. • We propose a selective sampling framework that efficiently identifies representative data points which effectively approximate the original feature space. • We report extensive experimental results on synthetic and real-world datasets to demonstrate the effectiveness of our method against the state-of-the-art subspace clustering methods.
Keywords:
Scalable subspace clustering
Self-expressive model
Subsampling
Big data
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