Pairwise Constraints Multidimensional Scaling for Discriminative Feature Learning

Linghao Zhang, Bo Pang, Haitao Tang, Hongjun Wang (), Chongshou Li and Zhipeng Luo
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Linghao Zhang: State Gid Sichuan Electric Power Research Institute, Power Internet of Things Key Laboratory of Sichuan Province, Chengdu 610094, China
Bo Pang: State Gid Sichuan Electric Power Research Institute, Power Internet of Things Key Laboratory of Sichuan Province, Chengdu 610094, China
Haitao Tang: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611731, China
Hongjun Wang: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611731, China
Chongshou Li: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611731, China
Zhipeng Luo: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611731, China

Mathematics, 2022, vol. 10, issue 21, 1-16

Abstract: As an important data analysis method in the field of machine learning and data mining, feature learning has a wide range of applications in various industries. The traditional multidimensional scaling (MDS) maintains the topology of data points in the low-dimensional embeddings obtained during feature learning, but ignores the discriminative nature between classes of low-dimensional embedded data. Thus, the discriminative multidimensional scaling based on pairwise constraints for feature learning (pcDMDS) model is proposed in this paper. The model enhances the discriminativeness from two aspects. The first aspect is to increase the compactness of the new data representation in the same cluster through fuzzy k -means. The second aspect is to obtain more extended pairwise constraint information between samples. In the whole feature learning process, the model considers both the topology of samples in the original space and the cluster structure in the new space. It also incorporates the extended pairwise constraint information in the samples, which further improves the model’s ability to obtain discriminative features. Finally, the experimental results on twelve datasets show that pcDMDS performs 10.31 % and 8.31 % higher than PMDS model in terms of accuracy and purity.

Keywords: discriminative feature learning; multidimensional scaling; fuzzy k-means; pairwise constraint propagation; iterative majorization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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