Sparse representation is being proved to be effective for many tasks in the field of pattern recognition. In this paper, an efficient classification algorithm based on concentrative sparse representation will be
Sparse representation classification (SRC)Sparse coefficientWeed recognition in field is a challenging and hard research field, due to the diversity and changeability of the weed in field. A weed recognition approach is proposed by sparse representation classification (SRC). The method is different ...
Sparse representation-based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum \\( l_{ 1} \\) -norm on coding coefficient vector, which requires high computational cost. On the other hand, supervised sparse ...
Sparse representation based classification (SRC) has been shown to be an effective method for face recognition. Furthermore, the input features of more and more classifiers are extracted by dimensional reduction methods. However, we find that the reconstruction ability of a basis for a testing ...
Sparse representation based classification (SRC) has been proved to be a simple, effective and robust solution to face recognition. As it gets popular, doubts on the necessity of enforcing sparsity starts coming up, and primary experimental results showed that simply changing the $l_1$-norm ...
A classification strategy Later we apply our method to experimental data. In our first application, we analyse multi-subject EEG data from the DEAP emotion database (section “Analysis of DEAP emotion dataset”). Network measures based on the resulting networks are used as features to classify su...
Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (...
The sparse representation or sparse coding based image reconstruction and classification has been successfully used in face recognition. Inspired by the sparsity of biological vision, Wright et al. [1] presented the sparse representation-based classifier (SRC), which can be successfully applied in unco...
In Section 2, we review briefly some related works on sparse representation based classification as well as projection learning. Then we formulate the discriminative projection and dictionary learning model for SRC and present an effective learning algorithm in Section 3. Extensive experiments are ...
Sparse representation-based classification (SRC) is one of the most effective face recognition techniques. Under this framework, we proposed a robust face recognition method by using a low-rank dictionary decomposition technique and a low-rank projection matrix learning method. A class-specific dictiona...