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A new multi-view learning machine with incomplete data

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Abstract

Multi-view learning with incomplete views (MVL-IV) is a reliable algorithm to process incomplete datasets which consist of instances with missing views or features. In MVL-IV, it exploits the connections among multiple views and suggests that different views are generated from a shared subspace such that it can recover the missing views or features well while MVL-IV neglects two facts. One is that different views should always be generated from different subspaces. The other is that the information of view-based classifiers is useful to the design of MVL-IV. Thus, on the base of MVL-IV, we consider these two facts and develop a new multi-view learning with incomplete data (NMVL-IV). Related experiments on clustering, regression, classification, bipartite ranking, and image retrieval have validated that the proposed NMVL-IV can recover the incomplete data much better.

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Notes

  1. In MVL-IV, incomplete-view case includes missing views, missing features, and missing data

  2. Since the new machine is developed on the base of MVL-IV which can recover incomplete data, for consistency, the new machine is also abbreviated as NMVL-IV rather than NMVL-ID.

  3. SR-LS: self-representation-based matrix completion by least-square, SR-LR: self-representation-based matrix completion by low-rank, SR-Sp: self-representation-based matrix completion by sparse self-representations

  4. Downloaded from ‘http://archive.ics.uci.edu/ml/datasets/Multiple+Features.’

  5. Downloaded from ‘http://archive.ics.uci.edu/ml/datasets/Reuters+RCV1+RCV2+Multilingual%2C+Multiview+Text+Categorization+Test+collection.’

  6. Downloaded from ‘http://archive.ics.uci.edu/ml/datasets/Corel+Image+Features.’

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Acknowledgements

This work is supported by National Natural Science Foundation of China (CN) under Grant Number 61602296, Natural Science Foundation of Shanghai under Grant Number 16ZR1414500, Project funded by China Postdoctoral Science Foundation under Grant Number 2019M651576. Furthermore, this work is also sponsored by ‘Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under Grant Number 18CG54. The authors would like to thank their supports.

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Changming Zhu, Chao Chen, Rigui Zhou, Lai Wei & Xiafen Zhang

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  1. Changming Zhu
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Correspondence to Changming Zhu.

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Zhu, C., Chen, C., Zhou, R. et al. A new multi-view learning machine with incomplete data. Pattern Anal Applic 23, 1085–1116 (2020). https://doi.org/10.1007/s10044-020-00863-y

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