A sparse representation-based two-phase classification algorithm is proposed for off-line handwritten Tibetan character recognition. The first phase realizes coarse classification with the K-NN classifier by fi
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...
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 ...
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 ...
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 ...
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 ...
Kernel Sparse Representation-Based Classifier. IEEE Trans. Signal Process. 2012, 60, 1624–1632. [Google Scholar] [CrossRef] Li, Y.; Ngom, A. Classification Approach Based on Non-Negative Least Squares. Neurocomput 2013, 118, 41–57. [Google Scholar] [CrossRef] Li, Y.; Ngom, A. ...
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 ...