Name
Title | ||
|---|---|---|
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods. |
Abstract | ||
|---|---|---|
Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data. This paper is concerned with the consistency of optimization-based techniques for such problems, in the limit where the labels have small noise and the underlying unlabelled data is well clustered. We study graph-based probit for binary classification, and a natural generalization of this method to multi-class classification using one-hot encoding. The resulting objective function to be optimized comprises the sum of a quadratic form defined through a rational function of the graph Laplacian, involving only the unlabelled data, and a fidelity term involving only the labelled data. The consistency analysis sheds light on the choice of the rational function defining the optimization. |
Year | Venue | Keywords |
|---|---|---|
2019 | JOURNAL OF MACHINE LEARNING RESEARCH | Semi-supervised learning,classification,consistency,graph Laplacian,probit,spectral analysis |
DocType | Volume | Issue |
Journal | 21 | 186 |
ISSN | Citations | PageRank |
1532-4435 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franca Hoffmann | 1 | 0 | 0.34 |
| Bamdad Hosseini | 2 | 0 | 1.01 |
| Zhi Ren | 3 | 0 | 0.34 |
| Andrew M. Stuart | 4 | 324 | 85.31 |