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Robust Multi-view Features Fusion Method Based on CNMF

  • Bangjun Wang
  • Liu Yang
  • Li Zhang
  • Fanzhang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

Multi-view feature fusion should be expected to mine implicit nature relationships among multiple views and effectively combine the data presented by multiple views to obtain the new feature representation of the object using a right model. In practical applications, Collective Matrix Factorization (CMF) has good effects on the fusion of multi-view data, but for noise-containing situations, the generalization ability is poor. Based on this, the paper came up with a Robust Collective Non-negative Matrix Factorization (RCNMF) model which can learn the shared feature representation of multi-view data and denoise at the same time. Based on several public data sets, experimental results fully demonstrate the effectiveness of the proposed method.

Keywords

Multi-view CMF Future fusion 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic ComputingSoochow UniversitySuzhouChina
  2. 2.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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