Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:25:57.445337Z
Paper Citation Record · LEDGER
As of 16 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:1908.07956.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:25:57.445337Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
63 of 63 outbound references displayed
External citation measurements
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Non-negative Sparse and Collaborative Representation for Pattern Classification A probabilistic collaborative representation based approach for pattern classification
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Non-negative Sparse and Collaborative Representation for Pattern Classification Acquiring linear subspaces for face recog- nition under variable lighting
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Non-negative Sparse and Collaborative Representation for Pattern Classification Classification and boosting with multiple collaborative representations
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Non-negative Sparse and Collaborative Representation for Pattern Classification Sparse, collaborative, or nonnegative repre- sentation: Which helps pattern classification? Pattern Recognition, 88:679 – 688, 2019
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Non-negative Sparse and Collaborative Representation for Pattern Classification Learning the parts of objects by non-negative matrix factor- ization
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Non-negative Sparse and Collaborative Representation for Pattern Classification Why is facial occlusion a challenging problem? In Advances in Biometrics: Third International Conference , pages 299–308, Berlin, Heidelberg, 2009
Reference 24
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Non-negative Sparse and Collaborative Representation for Pattern Classification Rigamonti, M
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Non-negative Sparse and Collaborative Representation for Pattern Classification Regularization and variable selection via the elastic net
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Non-negative Sparse and Collaborative Representation for Pattern Classification Regression shrinkage and selection via the lasso
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Non-negative Sparse and Collaborative Representation for Pattern Classification Gradient-based learning applied to document recognition
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