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Sparse Representation-based Classification

Sparse Representation-based Classification is the task based on the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements.

Papers

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Showing 1–10 of 49 papers

TitleStatusHype
Associative TransformerCode0
SparseFormer: Sparse Visual Recognition via Limited Latent TokensCode1
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG SurveillanceCode1
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification—0
Sparse Recovery via Bootstrapping: Collaborative or Independent?—0
Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Multiplication fusion of sparse and collaborative-competitive representation for image classificationCode0
Collaborative representation-based robust face recognition by discriminative low-rank representation—0
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