Semi-supervised learning using hidden feature augmentation

Wenlong Hang, Kup Sze Choi, Shitong Wang, Pengjiang Qian

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Semi-supervised learning methods are conventionally conducted by simultaneously utilizing abundant unlabeled samples and a few labeled samples given. However, the unlabeled samples are usually adopted with assumptions, e.g., cluster and manifold assumptions, which degrade the performance when the assumptions become invalid. The reliable hidden features embedded in both the labeled and the unlabeled samples can potentially be used to tackle this issue. In this regard, we investigate the feature augmentation technique to improve the robustness of semi-supervised learning in this paper. By introducing an orthonormal projection matrix, we first transform both the unlabeled and labeled samples into a shared hidden subspace to determine the connections between the samples. Then we utilize the hidden features, the raw features, and zero vectors determined to develop a novel feature augmentation strategy. Finally, a hidden feature transformation (HTF) model is proposed to compute the desired projection matrix by applying the maximum joint probability distribution principle in the augmented feature space. The effectiveness of the proposed method is evaluated in terms of the hinge and square loss functions respectively, based on two types of semi-supervised classification formulations developed using only the labeled samples with their original features and hidden features. The experimental results have demonstrated the effectiveness of the proposed feature augmentation technique for semi-supervised learning.
Original languageEnglish
Pages (from-to)448-461
Number of pages14
JournalApplied Soft Computing Journal
Volume59
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • Cluster assumption
  • Hidden features
  • Joint probability distribution
  • Manifold assumption
  • Semi-supervised learning

ASJC Scopus subject areas

  • Software

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