Multi-View Missing Data Completion

Lei Zhang, Yao Zhao, Zhenfeng Zhu, Dinggang Shen, Shuiwang Ji

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

A growing number of multi-view data arises naturally in many scenarios, including medical diagnosis, webpage classification, and multimedia analysis. A challenge in learning from multi-view data is that not all instances are fully represented in all views, resulting in missing view data. In this paper, we focus on feature-level completion for missing view of multi-view data. Aiming at capturing both semantic complementarity and identical distribution among different views, an Isomorphic Linear Correlation Analysis (ILCA) method is proposed to linearly map multi-view data to a feature-isomorphic subspace through learning a set of excellent isomorphic features, thereby unfolding the shared information from different views. Meanwhile, we assume that missing view obeys normal distribution. Then, the missing view data matrix can be modeled as a low-rank component plus a sparse contribution. Thus, to accomplish missing view completion, an Identical Distribution Pursuit Completion (IDPC) model based on the learned features is proposed, in which the identical distribution constraint of missing view to the other available one in the feature-isomorphic subspace is fully exploited. Comprehensive experiments on several multi-view datasets demonstrate that our proposed framework yields promising results.

Original languageEnglish
Pages (from-to)1296-1309
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number7
DOIs
Publication statusPublished - 2018 Jul 1

Keywords

  • Multi-view learning
  • feature-level completion
  • missing view
  • optimization
  • sparse learning
  • trace norm

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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