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A Supervised Laplacian Eigenmap Algorithm for Visualization of Multi-label Data: SLE-ML

  • Mariko TaiEmail author
  • Mineichi Kudo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

A novel supervised Laplacian eigenmap algorithm is proposed especially aiming at visualization of multi-label data. Supervised Laplacian eigenmap algorithms proposed so far suffer from hardness in the setting of parameters or the lack of the ability of incorporating the label space information into the feature space information. Most of all, they cannot deal with multi-label data. To cope with these difficulties, we consider the neighborhood relationship between two samples both in the feature space and in the label space. As a result, multiple labels are consistently dealt with as the case of single labels. However, the proposed algorithm may produce apparent/fake separability of classes. To mitigate such a bad effect, we recommend to use two values of the parameter at once. The experiments demonstrated the advantages of the proposed method over the compared four algorithms in the visualization quality and understandability, and in the easiness of parameter setting.

Keywords

Supervised Laplacian eigenmap Multi-Label data Feature and label spaces 

Notes

Acknowledgment

This work was partially supported by JSPS KAKENHI Grant Number 19H04128.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

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