Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations

Runxin Li, Jiaxing Du, Jiaman Ding, Lianyin Jia, Yinong Chen and Zhenhong Shang ()
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Runxin Li: Yunnan Key Lab of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500, China
Jiaxing Du: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Jiaman Ding: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Lianyin Jia: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Yinong Chen: School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA
Zhenhong Shang: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Mathematics, 2023, vol. 11, issue 3, 1-25

Abstract: The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes. Many methods only focus attention on label correlations and ignore the instance interrelations between the original feature space and low dimensional space. Additionally, very few techniques consider how to constrain the projection matrix to identify specific and common features in the feature space. In this paper, we propose a new approach of semi-supervised multi-label dimensionality reduction learning by instance and label correlations (SMDR-IC, in short). Firstly, we reformulate MDDM which incorporates label correlations as a least-squares problem so that the label propagation mechanism can be effectively embedded into the model. Secondly, we investigate instance correlations using the k -nearest neighbor technique, and then present the l 1 -norm and l 2 , 1 -norm regularization terms to identify the specific and common features of the feature space. Experiments on the massive public multi-label data sets show that SMDR-IC has better performance than other related multi-label dimensionality reduction methods.

Keywords: semi-supervised learning; multi-label learning; dimensionality reduction; instance correlations; common features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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