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Enhanced Tensor Low-Rank and Sparse Representation Recovery for Incomplete Multi-View Clustering

AAAI 2023 · Learning theory and algorithms › Matrix and tensor methods · approach: Low-rank tensor completion · 81 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Jointly learn incomplete graphs and recover an intrinsic tensor with nonconvex low-rank and structured-sparse regularizationETLSRR · ORL, p=0.1ACC: 96.00 -> 96.55Helped
Jointly learn incomplete graphs and recover an intrinsic tensor with nonconvex low-rank and structured-sparse regularizationETLSRR · BBCSport, p=0.1ACC: 96.27 -> 99.60Helped
Jointly learn incomplete graphs and recover an intrinsic tensor with nonconvex low-rank and structured-sparse regularizationETLSRR · RGB-D, p=0.1ACC: 49.36 -> 60.80Helped
Jointly learn incomplete graphs and recover an intrinsic tensor with nonconvex low-rank and structured-sparse regularizationETLSRR · UCI, p=0.1ACC: 99.60 -> 99.80Helped
Jointly learn incomplete graphs and recover an intrinsic tensor with nonconvex low-rank and structured-sparse regularizationETLSRR · Scene, p=0.1ACC: 76.43 -> 94.39Helped
Replace the Laplace nonconvex low-rank regularizer with the convex tensor nuclear norm (ablation)ETLSRR · ORL, varying missing ratesclustering performanceHurt
Remove the structured tensor sparsity regularization (ablation)ETLSRR · ORL, varying missing ratesclustering performanceHurt
Replace the Laplace low-rank function with the ℓp function (ablation)ETLSRR · ORL, varying missing ratesclustering performanceMixed
Replace the Laplace low-rank function with the logarithm function (ablation)ETLSRR · ORL, varying missing ratesclustering performanceMixed

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