A New Approach to Speeding Up Topic Modeling

Details A New Approach to Speeding Up Topic Modeling A New Approach to Speeding Up Topic Modeling

2012-04-01

arxiv.org/abs/1204.0170v1

Computer Science

Learning

12 pages, 11 figures

Scientific paper

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. This paper proposes a fast and accurate algorithm, active belief propagation (ABP), for training LDA. Usually training LDA requires repeated scanning of the entire corpus and searching the complete topic space. Confronted with massive corpus with large number of topics, such a training iteration is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans partial corpus and searches partial topic space for topic modeling, saving enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around 10 to 100 times faster than some of the major state-of-the-art algorithms for training LDA, while retains a comparable topic modeling accuracy.

Affiliated with

Also associated with

No associations

Rating

A New Approach to Speeding Up Topic Modeling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A New Approach to Speeding Up Topic Modeling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A New Approach to Speeding Up Topic Modeling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-552357