Deep semi-supervised classification based in deep clustering and cross-entropy

Self-labeled techniques, a semi-supervised classification paradigm (SSC), are highly effective in alleviating the scarcity of labeled data used in classification tasks through an iterative process of self-training. This problem was addressed by several approaches with different assumptions about the features of the input data, examples of these approaches being self-training, co-training, STRED, among others. This paper presents a framework for data self-labeling based on deep autoencoder combined with a self-labeled technique that takes into consideration cross-entropy. The model uses the Encoder to reduce the dimensionality of the input that is submitted to a labeling layer. The weights of this layer are adjusted through the learning from a clustering performed in the Z space, which is the reduced dimensionality space. Results showed that the proposed method obtained competitive performance in relation to classic methods that are found in the literature.

Automated summary

This paper introduces a framework for data self-labeling based on deep autoencoder combined with a self-labeled technique that takes into consideration cross-entropy. Clustering learning in the reduced dimensionality space Z helps adjust the weights of the labeling layer, and the proposed method achieves competitive performance compared to classic methods found in the literature.