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

摘要

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.