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- W2969694252 abstract "We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly simple model for subspace clustering with learning non-linear mapping. Compared with the previous deep subspace clustering (DSC) techniques, our DCFSC does not have any parameters at all for the self-expressive layer. Instead, DCFSC utilizes the implicit data-driven self-expressive layer derived from closed-form shallow auto-encoder. Moreover, DCFSC also has no complicated optimization scheme, unlike the other subspace clustering methods. With its extreme simplicity, DCFSC has significant memory-related benefits over the existing DSC method, especially on the large dataset. Several experiments showed that our DCFSC model had enough potential to be a new reference model for subspace clustering on large-scale high-dimensional dataset." @default.
- W2969694252 created "2019-08-29" @default.
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- W2969694252 date "2019-08-25" @default.
- W2969694252 modified "2023-09-24" @default.
- W2969694252 title "Deep Closed-Form Subspace Clustering" @default.
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- W2969694252 doi "https://doi.org/10.48550/arxiv.1908.09419" @default.
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