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01-01-2020 | MATHEMATICAL THEORY OF PATTERN RECOGNITION | Issue 1/2020

Pattern Recognition and Image Analysis 1/2020

Robust Feature Selection with Feature Correlation via Sparse Multi-Label Learning

Journal:
Pattern Recognition and Image Analysis > Issue 1/2020
Authors:
Jiangjiang Cheng, Junmei Mei, Jing Zhong, Min Men, Ping Zhong
Important notes
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010034/MediaObjects/11493_2020_6052_Fig6_HTML.gif
Jiangjiang Cheng was born in China, in 1998. He is majoring in Mathematics and Applied Mathematics in China Agricultural University, Beijing, China.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010034/MediaObjects/11493_2020_6052_Fig7_HTML.gif
Jing Zhong was born in China, in 1996. She has received the B.S. degree from Anhui Jianzhu University in 2017. Now she is studying for a master’s degree in China Agricultural University. Her research interests include machine learning and data mining.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010034/MediaObjects/11493_2020_6052_Fig8_HTML.gif
Ping Zhong is a professor and PhD supervisor in College of Science, China Agricultural University. She has published many papers. Her research interests include machine learning and support vector machines.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010034/MediaObjects/11493_2020_6052_Fig9_HTML.gif
Min Men was born in China, in 1997. She has received the B.S. degree from China Agricultural University in 2018. Now she is studying for a master’s degree in China Agricultural University. Her research interests include machine learning and data mining.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661820010034/MediaObjects/11493_2020_6052_Fig10_HTML.gif
Junmei Mei was born in China, in 1998. She is majoring in Mathematics and Applied Mathematics in China Agricultural University, Beijing, China.

Abstract

The multi-label feature selection that is regarded as a special case of multi-task learning has received much attention in recent years. In this paper, we propose a novel robust and pragmatic multi-label feature selection method, in which the joint l2,1-norm minimizations of loss function and regularization are emphasized. Specifically, the loss function based on the l2,1-norm is robust to outliers, and the l2,1-norm regularization selects features across all samples with joint sparsity. Besides, the feature information inherent in the data is used to construct the correlation matrix, which explores the correlation between features so as to remove the redundant features. An efficient algorithm based on the augmented Lagrangian multiplier method is proposed to solve the objective function. The extensive experiments compared with several state-of-the-art methods are performed on the multi-label datasets to show the effectiveness of the proposed method.

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