A Self-Training Hierarchical Prototype-Based Approach for Semi-Supervised Classification

Xiaowei Gu

Research output: Contribution to journalArticlepeer-review

28 Citations (SciVal)
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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.
Original languageEnglish
Pages (from-to)204-224
Number of pages21
JournalInformation Sciences
Volume535
Early online date13 May 2020
DOIs
Publication statusPublished - 01 Oct 2020
Externally publishedYes

Keywords

  • Classification
  • Hierarchical structure
  • Prototype-based
  • Self-training
  • Semi-supervised learning

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