Paper Info
Title
A self-training hierarchical prototype-based approach for semi-supervised classification.
Abstract
This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification. The proposed approach firstly identifies meaningful prototypes from labelled samples at multiple levels of granularity and, then, self-organizes a highly transparent, multi-layered recognition model by arranging them in a form of pyramidal hierarchies. After this, the learning model continues to self-evolve its structure and self-expand its knowledge base to incorporate new patterns recognized from unlabelled samples by exploiting the pseudo-label technique. Thanks to its prototype-based nature, the overall computational process of the proposed approach is highly explainable and traceable. Experimental studies with various benchmark image recognition problems demonstrate the state-of-the-art performance of the proposed approach, showing its strong capability to mine key information from unlabelled data for classification.
Year
DOI
Venue
2020
10.1016/j.ins.2020.05.018
Information Sciences
Keywords
DocType
Volume
Self-training,Prototype-based,Hierarchical structure,Semi-supervised learning,Classification
Journal
535
ISSN
Citations 
PageRank 
0020-0255
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Xiaowei Gu19910.96