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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 11–20 of 49 papers

TitleStatusHype
Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification—0
Learning a Representation with the Block-Diagonal Structure for Pattern Classification—0
A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample—0
Deep Sparse Representation-based ClassificationCode0
A Fast Dictionary Learning Method for Coupled Feature Space Learning—0
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition—0
The Use of Mutual Coherence to Prove ^1/^0-Equivalence in Classification Problems—0
Classifying Multi-channel UWB SAR Imagery via Tensor Sparsity Learning TechniquesCode0
An Integrated Inverse Space Sparse Representation Framework for Tumor Classification—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
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