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Enhancing Graph-Based Semisupervised Learning via Knowledge-Aware Data Embedding
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DOI：10.1109/TNNLS.2019.2955565.png)
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Abstract
En 中文
Semisupervised learning (SSL) is a family of classification methods conceived to reduce the amount of required labeled information in the training phase. Graph-based methods are among the most popular semisupervised strategies: the nearest neighbor graph is built in such a way that the manifold of the data is captured and the labeled information is propagated to target samples along the structure of the manifold. Research in graph-based SSL has mainly focused on two aspects: 1) the construction of the k-nearest neighbors graph and/or 2) the propagation algorithm providing the classification. Differently from the previous literature, in this article, we focus on the data representation with the aim of incorporating semisupervision earlier in the process. To this end, we propose an algorithm that learns a new knowledge-aware data embedding via an ensemble of semisupervised autoencoders to enhance a graph-based semisupervised classification. The experiments carried out on different classification tasks demonstrate the benefit of our approach.
Keywords:
Task analysis
Pipelines
Standards
Data models
Semisupervised learning
Training
Decoding
Autoencoders
data embedding
ensembles
semisupervised learning (SSL)
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