382. Laplacian Regularization in Semi-Supervised Learning with Functional Data
Invited abstract in session MB-5: Optimization and machine learning I, stream Optimization for machine learning.
Monday, 10:30-12:30Room: B100/4013
Authors (first author is the speaker)
| 1. | Zhengang Zhong
|
| Statistics, University of Warwick |
Abstract
We investigate a family of regression problems in a semi-supervised setting, where the goal is to assign real-valued labels to n sample points, given a small subset of N labeled points. A goal of semi-supervised learning is to leverage the (geometric) structure provided by the large number of unlabeled data when assigning labels. To capture this structure, we model the data set using random geometric graphs with a connection radius. In the context of optimization problems, the associated objective functionals reward the regularity of the labeling function. However, these problems degenerate in high dimensions when the labeling function lacks sufficient regularity. To address this issue, we study the point-wise asymptotic behavior of such objective functionals in the setting of functional sample data, as n goes to infinity and the connection radius tends to zero.
Keywords
- Optimization for learning and data analysis
Status: accepted
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