A Semi-Supervised Self-Organizing Map for Clustering and Classification
This addresses the challenge of leveraging unlabeled data in clustering and classification for domains with limited labeled data, but it is incremental as it builds on existing self-organizing map techniques.
The paper tackles the problem of semi-supervised learning for high-dimensional datasets with few labeled samples by proposing a new method called SS-SOM, which dynamically switches between supervised and unsupervised learning during training, and results show it outperforms other methods when labeled samples are low and performs well with all labeled samples.
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both clustering and classification. Also, these datasets commonly have a high number of dimensions. This article presents a new semi-supervised method based on self-organizing maps (SOMs) for clustering and classification, called Semi-Supervised Self-Organizing Map (SS-SOM). The method can dynamically switch between supervised and unsupervised learning during the training according to the availability of the class labels for each pattern. Our results show that the SS-SOM outperforms other semi-supervised methods in conditions in which there is a low amount of labeled samples, also achieving good results when all samples are labeled.