Weighted co-association rate-based Laplacian regularized label description for semi-supervised regression

Jaehong Yu, Youngdoo Son

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose a weighted co-association rate-based Laplacian regularized label description algorithm. In the proposed algorithm, we define a regression function by combining weighted co-association rates and a label descriptive function. We use the weighted co-association rate, computed by summarizing various clustering solutions, to depict the data structure. The label descriptive function identifies a latent label distribution, and hence helps the regression function to accurately involve as much true label information as possible. To derive the optimal label descriptive function, we apply the smoothness regularizer to label descriptive function. Experiments were conducted on various benchmark datasets to examine the properties of the proposed algorithms, and the results were compared with those of the existing methods. The experimental results confirm that the proposed algorithm outperforms the previous methods.

Original languageEnglish
Pages (from-to)688-712
Number of pages25
JournalInformation Sciences
Volume545
DOIs
StatePublished - 4 Feb 2021

Keywords

  • Label descriptive function
  • Semi-supervised regression
  • Smoothness regularization
  • Weighted co-association rate

Fingerprint

Dive into the research topics of 'Weighted co-association rate-based Laplacian regularized label description for semi-supervised regression'. Together they form a unique fingerprint.

Cite this