Pairwise Constraints Multidimensional Scaling for Discriminative Feature Learning

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.