Deep topic modeling by multilayer bootstrap network and lasso
This work addresses topic modeling for document analysis, but it appears incremental as it combines existing methods (MBN and Lasso) in a new application.
The authors tackled the difficult optimization problem in topic modeling by proposing a polynomial-time deep topic model that uses a multilayer bootstrap network for dimension reduction and Lasso for topic word discovery, achieving effectiveness as shown in experiments on 20-newsgroups and TDT2 corpora.
Topic modeling is widely studied for the dimension reduction and analysis of documents. However, it is formulated as a difficult optimization problem. Current approximate solutions also suffer from inaccurate model- or data-assumptions. To deal with the above problems, we propose a polynomial-time deep topic model with no model and data assumptions. Specifically, we first apply multilayer bootstrap network (MBN), which is an unsupervised deep model, to reduce the dimension of documents, and then use the low-dimensional data representations or their clustering results as the target of supervised Lasso for topic word discovery. To our knowledge, this is the first time that MBN and Lasso are applied to unsupervised topic modeling. Experimental comparison results with five representative topic models on the 20-newsgroups and TDT2 corpora illustrate the effectiveness of the proposed algorithm.