Online Chinese Restaurant Process

author: Chien-Liang Liu, Industrial Technology Research Institute (ITRI)
published: Oct. 7, 2014,   recorded: August 2014,   views: 2601
Categories

Slides

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.
  Delicious Bibliography

Description

Processing large volumes of streaming data in near-real-time is becoming increasingly important as the Internet, sensor networks and network traffic grow. Online machine learning is a typical means of dealing with streaming data, since it allows the classification model to learn one instance of data at a time. Although many online learning methods have been developed since the development of the Perceptron algorithm, existing online methods assume that the number of classes is available in advance of classification process. However, this assumption is unrealistic for large scale or streaming data sets. This work proposes an online Chinese restaurant process (CRP) algorithm, which is an online and nonparametric algorithm, to tackle this problem. This work proposes a relaxing function as part of the prior and updates the parameters with the likelihood function in terms of the consistency between the true label information and predicted result. This work presents two Gibbs sampling algorithms to perform posterior inference. In the experiments, the online CRP is applied to three massive data sets, and compared with several online learning and batch learning algorithms. One of the data sets is obtained from Wikipedia, which comprises approximately two million documents. The experimental results reveal that the proposed online CRP performs well and efficiently on massive data sets. Finally, this work proposes two methods to update the hyperparameter $\alpha$ of the online CRP. The first method is based on the posterior distribution of $\alpha$, and the second exploits the property of online learning, namely adapting to change, to adjust $\alpha$ dynamically.

See Also:

Download slides icon Download slides: kdd2014_liu_chinese_restaurant_01.pdf (1.2┬áMB)


Help icon Streaming Video Help

Link this page

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

Reviews and comments:

Comment1 Willson, November 22, 2019 at 7:10 a.m.:

Panda Express is the one of the biggest Chinese fast food restaurants in the world. I am a huge Chinese food lover and Every week 2 or 3 times I must visiting the fast food restaurants near me. Recently I won the meals at panda express restaurant by taking the official panda express feedback survey at https://pandaexpressfeedbacks.com/. Every panda express restaurant customer have a chance to get the meals for free by taking the official panda express feedback survey.

Write your own review or comment:

make sure you have javascript enabled or clear this field: