A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

Di Wu, Yi He, Xin Luo, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does not always hold in many real applications. Motivated by this observation, this study aims to implement online feature selection from sparse streaming features, i.e., features flow in one by one with missing data as instance count remains fixed. To do so, this study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA). Its main idea is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently. Theoretical and empirical studies indicate that LOSSA can significantly improve the quality of OSFS when missing data are encountered in target instances.

Original languageEnglish (US)
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Approximation algorithms
  • Big data
  • computational intelligence
  • Data models
  • Feature extraction
  • latent factor analysis (LFA)
  • missing data
  • online algorithm
  • online feature selection
  • Smart cities
  • Sparse matrices
  • sparse streaming feature
  • streaming feature.
  • Tensors
  • Training


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