Sparse Representation-based Classification
9 papers with code • 1 benchmarks • 1 datasets
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.
SVHN
Latest papers with no code
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Though there remains a small performance gap between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised learning.
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification
The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition.
Sparse Recovery via Bootstrapping: Collaborative or Independent?
However, this approach fails in challenging scenarios such as when the noise level is high or there are missing data / adversarial samples.
Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism
Sparse approximation is the problem to find the sparsest linear combination for a signal from a redundant dictionary, which is widely applied in signal processing and compressed sensing.
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification
The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios.
Collaborative representation-based robust face recognition by discriminative low-rank representation
Collaborative representation based classification (CRC) method is exploited in our proposed method which has closed-form solution.
Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification
This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM).
Learning a Representation with the Block-Diagonal Structure for Pattern Classification
To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition.
A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample
In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images.
A Fast Dictionary Learning Method for Coupled Feature Space Learning
In this letter, we propose a novel computationally efficient coupled dictionary learning method that enforces pairwise correlation between the atoms of dictionaries learned to represent the underlying feature spaces of two different representations of the same signals, e. g., representations in different modalities or representations of the same signals measured with different qualities.
