Semi-supervised One-Pass Multi-view Learning with Variable Features and Views

  • Changming ZhuEmail author
  • Duoqian Miao


Traditional supervised multi-view learning machines aim to process multi-view data sets which consist of labeled instances from multiple views. While they cannot deal with semi-supervised data sets whose training instances consist of both labeled and unlabeled ones. Moreover, with the limitation of storage and process ability, some learning machines cannot process large-scale data sets. Furthermore, some instances maybe have missing features or views and traditional multi-view learning machines have no ability to process the data sets with variable features and views. Thus, this paper develops a semi-supervised one-pass multi-view learning with variable features and views (SOMVFV) so as to process the large-scale semi-supervised data sets with variable features and views. Related experiments on some supervised, semi-supervised, large-scale, and small-scale data sets validate the effectiveness of our proposed SOMVFV and we can get the following conclusions, (1) SOMVFV can process multiple kinds of special data sets; (2) compared with most learning machines used in our experiments, the better performance of SOMVFV is significant; (3) compared with missing views, missing features has a greater influence on the classification accuracy.


Semi-supervised multi-view learning Variable views Variable features One-pass learning 



This work is supported by National Natural Science Foundation of China under Grant numbers 61602296 and 41701523, Natural Science Foundation of Shanghai under Grant number 16ZR1414500 and authors would like to thank their supports.


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Authors and Affiliations

  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Computer Science and TechnologyTongji UniversityShanghaiPeople’s Republic of China

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