Deep Belief Nets Topic Modeling
|Abstract||This thesis is conducted in collaboration with Issuu, an online publishing company. In order to analyze the vast amount of documents on the platform, Issuu use Latent Dirichlet Allocation as a topic model.|
Geoffrey Hinton & Ruslan Salakhutdinov have introduced a new way to perform topic modeling, which they claim can outperform Latent Dirichlet Allocation. The topic model is based on the theory of Deep Belief Nets and is a way of computing the conceptual meaning of documents into a latent representation. The latent representation consists of a reduced dimensionality of binary numbers, which proves to be useful when comparing documents.
The thesis comprises the development of a toolbox for the Deep Belief Nets for topic modeling by which performance measurements has been conducted on the model itself and as a comparison to Latent Dirichlet Allocation.
|Type||Master's thesis [Academic thesis]|
|Publisher||Technical University of Denmark, Department of Applied Mathematics and Computer Science|
|Address||Matematiktorvet, Building 303B, DK-2800 Kgs. Lyngby, Denmark, email@example.com|
|Series||DTU Compute M.Sc.-2014|
|Note||DTU supervisor: Ole Winther, firstname.lastname@example.org, DTU Compute, external supervisor Morten Arngren from Issuu|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|