Uncertainty-Based Active Learning via Sparse Modeling for Image Classification.
IEEE Trans Image Process
; 28(1): 316-329, 2019 Jan.
Article
en En
| MEDLINE
| ID: mdl-30176591
ABSTRACT
Uncertainty sampling-based active learning has been well studied for selecting informative samples to improve the performance of a classifier. In batch-mode active learning, a batch of samples are selected for a query at the same time. The samples with top uncertainty are encouraged to be selected. However, this selection strategy ignores the relations among the samples, because the selected samples may have much redundant information with each other. This paper addresses this problem by proposing a novel method that combines uncertainty, diversity, and density via sparse modeling in the sample selection. We use sparse linear combination to represent the uncertainty of unlabeled pool data with Gaussian kernels, in which the diversity and density are well incorporated. The selective sampling method is proposed before optimization to reduce the representation error. To deal with ${l}_{0}$ norm constraint in the sparse problem, two approximated approaches are adopted for efficient optimization. Four image classification data sets are used for evaluation. Extensive experiments related to batch size, feature space, seed size, significant analysis, data transform, and time efficiency demonstrate the advantages of the proposed method.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Image Process
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article