Multi-geometric Sparse Subspace Clustering

Hu, Wenbo; Wu, Xiao-Jun

doi:10.1007/s11063-020-10274-z

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…Differently, global-based subspace clustering methods first utilize a self-expression model to learn an affinity matrix, and then use it to construct a similarity matrix. Examples include Sparse Subspace Clustering (SSC) [5,6,10,23,27], Low Rank (LR) subspace clustering [5,15,16] and Least Square Regression (LSR) [11,19,29]. In practice, the affine matrix returned by self-expression model can reflect the similarity among all instances.…”

Section: Introductionmentioning

confidence: 99%

Subspace Clustering via Integrating Sparse Representation and Adaptive Graph Learning

Deng

Huang

et al. 2021

Neural Process Lett

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Sparse representation is a powerful tool for subspace clustering, but most existing methods for this issue ignore the local manifold information in learning procedure. To this end, in this paper we propose a novel model, dubbed Sparse Representation with Adaptive Graph (SRAG), which integrates adaptive graph learning and sparse representation into a unified framework. Specifically, the former can preserve the local manifold structure of data, while the latter is useful for digging global information. For the objective function of SRAG has multiple intractable terms, an ADMM method is developed to solve it. Numerous experimental results demonstrate that our proposed method consistently outperforms several representative clustering algorithms by significant margins.

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