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Inductive Multi-View Semi-supervised Learning with a Consensus Graph
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DOI：10.1007/s12559-023-10123-w.png)
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Graphs have a crucial impact on the performance of any graph-based semi-supervised learning method, so their construction should be carefully considered. In this letter, and in the context of semi-supervised learning, we will address graph-based semi-supervised learning employing multiple views for the data. The notion of data smoothness is another missing concept in graph construction that should be considered when constructing graphs. Compared to a single feature, using multiple sources of information can increase the efficiency of the post-processing task that adopts the constructed graph. Therefore, we present an approach that merges the notions of data smoothness and label smoothness with label fitness and projection matrix calculation. Moreover, two or more views are merged to exploit the information hidden in different features. Experiments performed with image databases show the superiority of the proposed approach compared to single features and other competing fusion algorithms. Compared to recent fusion methods, the introduced scheme improved the semi-supervised classification performance. For example, on the MNIST dataset with 20 labeled images per class, the average improvement due to the proposed labeling inference was 4.4%. The proposed method is inductive and computes a linear mapping to obtain the label of unseen or test patterns.
Keyword:
Semi-supervised learning
Multiview learning
Graph construction
Information fusion
Data smoothness
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1.6K
被引数:
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