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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
SparseFormer: Sparse Visual Recognition via Limited Latent TokensCode1
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG SurveillanceCode1
Associative TransformerCode0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
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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