Revisiting and Improving Semi-supervised Learning: A Large Dimensional Approach

Xiaoyi Mai; Romain Couillet

ICASSP 2019accepted0 citations

Revisiting and Improving Semi-supervised Learning: A Large Dimensional Approach

Abstract

The recent work [1] shows that in the big data regime (i.e., numerous high dimensional data), the popular semi-supervised graph regularization, known as semi-supervised Laplacian regularization, fails to effectively extract information from unlabelled data. In response to this problem, we propose in this article an improved approach based on a simple yet fundamental update of the classical method. The effectiveness of the former is supported by both asymptotic results and simulations on finite data samples.

BibTeX

@inproceedings{icassp2019_revisitingandimp,
  title = {Revisiting and Improving Semi-supervised Learning: A Large Dimensional Approach},
  author = {Xiaoyi Mai and Romain Couillet},
  booktitle = {ICASSP 2019},
  year = {2019}
}