title: Convergence rate of the semi-supervised greedy algorithm.
abstract: This paper proposes a new greedy algorithm combining the semi-supervised learning and the sparse representation with the data-dependent hypothesis spaces. The proposed greedy algorithm is able to use a small portion of the labeled and unlabeled data to represent the target function, and to efficiently reduce the computational burden of the semi-supervised learning. We establish the estimation of the generalization error based on the empirical covering numbers. A detailed analysis shows that the error has O(n(-1)) decay. Our theoretical result illustrates that the unlabeled data is useful to improve the learning performance under mild conditions.
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, China. chenhongml@163.com
authors.forename: Hong
authors.initials: H
authors.lastname: Chen
authors.forename: Yicong
authors.initials: Y
authors.lastname: Zhou
authors.forename: Yuan Yan
authors.initials: YY
authors.lastname: Tang
authors.forename: Luoqing
authors.initials: L
authors.lastname: Li
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D013997:Time Factors