Kernel Sparse Representation-Based Classifier

By Li Zhang, Weida Zhou, Pei-Chann Chang, Jing Liu, Zhe Yan, Ting Wang, Fanzhang Li. IEEE Transactions on Signal Processing, 2012

327 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b944cc4241d195b1609a7a9d87fce0e9ba1498bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Vishal M. Patel
2. Shenghua Gao
3. Zechao Li
4. He Zhang
5. Jinhui Tang
6. Poojan Oza

In Computer Vision and Pattern Recognition

1. Vishal M. Patel
2. He Zhang
3. Jinhui Tang
4. Zechao Li
5. Shenghua Gao
6. Lu Jin
7. Vishwanath A. Sindagi
8. Ming-Hsuan Yang

In Signal Processing

1. He Zhang
2. Shenghua Gao
3. Vishal M. Patel
4. Zechao Li
5. Jinhui Tang
6. Ramalingam Chellappa

Citations in the corpus

• Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition (2014, methodology): “The commonly used feature encoding includes the sparse coding based feature encoding and its variance, for example, sparse coding [22], LLC [26], kernel sparse representation[14], [31].”
• Sparse Representation-Based Open Set Recognition (2017, methodology): “Furthermore, non-linear kernel extensions of the SRCmethod have also been proposed in [7], [8], [9], [10].”