Semi-supervised learning with auto-weighting feature and adaptive graph

Feiping Nie, Shaojun Shi, Xuelong Li

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Traditional graph-based Semi-Supervised Learning (SSL) methods usually contain two separate steps. First, constructing an affinity matrix. Second, inferring the unknown labels. While such a two-step method has been successful, it cannot take full advantage of the correlation between affinity matrix and label information. In order to address the above problem, we propose a novel graph-based SSL method. It can learn the affinity matrix and infer the unknown labels simultaneously. Moreover, feature selection with auto-weighting is introduced to extract the effective and robust features. Further, the proposed method learns the data similarity matrix by assigning the adaptive neighbors for each data point based on the local distance. We solve the unified problem via an alternative minimization algorithm. Extensive experimental results on synthetic data and benchmark data show that the proposed method consistently outperforms the state-of-the-art approaches.

Original languageEnglish
Article number8653311
Pages (from-to)1167-1178
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number6
DOIs
StatePublished - 1 Jun 2020

Keywords

  • Adaptive neighborhood
  • Auto-weighting feature
  • Graph-based semi-supervised learning
  • Label propagation

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