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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 21–30 of 49 papers

TitleStatusHype
Discriminant Projection Representation-based Classification for Vision Recognition—0
Color Face Recognition using High-Dimension Quaternion-based Adaptive Representation—0
Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images—0
Adaptive Class Preserving Representation for Image Classification—0
Sparse Representation-based Open Set RecognitionCode0
Linear Disentangled Representation Learning for Facial ActionsCode0
Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples—0
Sparse Representation-Based Classification: Orthogonal Least Squares or Orthogonal Matching Pursuit?—0
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis—0
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices—0
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