Multiview Boosting With Information Propagation for Classification

Jing Peng, Alex J. Aved, Guna Seetharaman, Kannappan Palaniappan

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Multiview learning has shown promising potential in many applications. However, most techniques are focused on either view consistency, or view diversity. In this paper, we introduce a novel multiview boosting algorithm, called Boost.SH, that computes weak classifiers independently of each view but uses a shared weight distribution to propagate information among the multiple views to ensure consistency. To encourage diversity, we introduce randomized Boost.SH and show its convergence to the greedy Boost.SH solution in the sense of minimizing regret using the framework of adversarial multiarmed bandits. We also introduce a variant of Boost.SH that combines decisions from multiple experts for recommending views for classification. We propose an expert strategy for multiview learning based on inverse variance, which explores both consistency and diversity. Experiments on biometric recognition, document categorization, multilingual text, and yeast genomic multiview data sets demonstrate the advantage of Boost.SH (85%) compared with other boosting algorithms like AdaBoost (82%) using concatenated views and substantially better than a multiview kernel learning algorithm (74%).

Original languageEnglish
Pages (from-to)657-669
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number3
StatePublished - Mar 2018


  • Biometrics
  • boosting
  • classification
  • convergence
  • data fusion
  • multiarmed bandits
  • multiview learning


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