Fully Distributed EM for Very Large Datasets

author: Jason Wolfe, UC Berkeley
published: July 30, 2008,   recorded: July 2008,   views: 3578

See Also:

Download slides icon Download slides: icml08_wolfe_fdem_01.pdf (1.6┬áMB)

Help icon Streaming Video Help

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of the parameters in a single node for the M-step can be impractical. We present a framework which fully distributes the entire EM procedure. Each node interacts with only parameters relevant to its data, sending messages to other nodes along a junction-tree topology. We demonstrate improvements over a MapReduce approach, on two tasks: word alignment and topic modeling.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: