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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
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG SurveillanceCode1
SparseFormer: Sparse Visual Recognition via Limited Latent TokensCode1
Adaptive Class Preserving Representation for Image Classification—0
Collaborative Representation based Classification for Face Recognition—0
A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample—0
A Fast Dictionary Learning Method for Coupled Feature Space Learning—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
An Integrated Inverse Space Sparse Representation Framework for Tumor Classification—0
Collaborative representation-based robust face recognition by discriminative low-rank representation—0
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