Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

ABSTRACT: Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bern

SUBMITTER: Wei C 

PROVIDER: S-EPMC4718658 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature