Robust Principal Component Analysis Based on Maximum Correntropy Criterion

By Ran He, Bao-Gang Hu, Weishi Zheng, Xiangwei Kong. IEEE Transactions on Image Processing, 2011

350 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8a09668efc95eafd6c3056ff1f0fbc43bb5774db

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Yu Qi
2. Zhaohui Wu
3. Xiaoxiang Zheng
4. Yueming Wang

In Signal Processing

1. Yu Qi
2. Zhaohui Wu
3. Xiaoxiang Zheng
4. Yueming Wang

Citations in the corpus

• Robust feature learning by stacked autoencoder with maximum correntropy criterion (2014, methodology): “[14] proposed a robust principal component analysis method based on maximum correntropy criterion to achieve high performance under outliers.”