Improving Large-Scale k-Nearest Neighbor Text Categorization with Label
  Autoencoders
Francisco J. Ribadas-Pena Shuyuan Cao Víctor M. Darriba Bilbao
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 large portion of the MEDLINE
biomedical document collection which uses the Medical Subject Headings (MeSH)
thesaurus as a controlled vocabulary. In our experiments we propose and
evaluate several document representation approaches and different label
autoencoder configurations.