In this paper, we propose the Group Sparse Representation based Classifier (GSRC) which removes the requirement for a separate feature-level fusion mechanism and integrates multi-feature representation seamlessly into classification. The performance of the proposed algorithm is evaluated on two multimodal ...
In this paper, we propose to learn simultaneously a discriminative projection and a dictionary that are optimized for the sparse representation based classifier, to extract discriminative information from the raw data while respecting the sparse representation assumption. By formulating the task of ...
Then, we provide a convergent analysis and a model extension on incomplete sparse representation. Finally, we conduct experiments on two real-world face datasets and compare the proposed method with the nearest neighbor classifier and the sparse representation-based classification. The experimental ...
In recent years, sparse representation-based classification (SRC) has made great progress in face recognition (FR). However, SRC emphasizes noise sparsity too much and it is not suitable for the real world. In this paper, we propose a robust l2,1-norm Sparse Representation framework that const...
Zhang L, Zhou W, Li F (2015) Kernel sparse representation-based classifier ensemble for face recognition. Multimed Tools Appl 74:123–137 Article Google Scholar Zhang D, Meng Y, Zhao F et al (2010) On the dimensionality reduction for sparse representation based face recognition. ICPR, pp ...
In this paper, based on sparse representation classification and robust thought, we propose a new classifier, named MRSRC (Metasample Based Robust Sparse Representation Classificatier), for DNA microarray data classification. Firstly, we extract Metasample from trainning sample. Secondly, a weighted ...
Since actual networks that are large also tend to be sparse20,21,22, obtaining a sparse representation of the network from high dimensional data becomes critical. In the context of neuronal networks, a primary reason for sparsity is the well-known fact23,24 that more than 50% of brain’s ...
Sparse representation-based classification (SRC) method has gained great success in face recognition due to its encouraging and impressive performance. However, in SRC the data used to train or test are usually corrupted, and hence the performance is affected. This paper proposes a robust face reco...
These kernel algorithms were actually proposed to improve the performance of sparse representation based classifier (SRC) [27], [33] which uses the linear modeling framework, by exploiting the advantages of projecting data in some high-dimensional space [15]. However, linear representations are ...
Sparse representation-based classification (SRC), which is pioneered by Wright et al. [16] for face recognition, has become another widely used hyperspectral classifier [17,18,19,20,21,22,23,24,25]. The SRC is suitable for classifying the HSIs because it relies on the assumption that the...