Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders

In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a …

algorithm autoencoder autoencoders correlation cs.cl cs.ir cs.lg deal document evolution indexing lazy neighbors paper scale semantic text