2012/06/27 by Ming Ji, Ji Ming, Ji, Ming +8 · 3 citations
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Face and Expression Recognition #Image Processing Techniques and Applications #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1206.6412
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)
arxiv created 2012/06/27 · openalex publication_date 2012/06/27 · arxiv updated 2012/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.