Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams
published: Sept. 27, 2015, recorded: July 2015, views: 192
Report a problem or upload filesIf 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.
Low-rank tensor learning has many applications in machine learning. A series of batch learning algorithms have achieved great successes. However, in many emerging applications, such as climate data analysis, we are confronted with largescale tensor streams, which pose significant challenges to existing solutions. In this paper, we propose an accelerated online low-rank tensor learning algorithm (ALTO) to solve the problem. At each iteration, we project the current tensor to a low-dimensional tensor, using the information of the previous low-rank tensor, in order to perform efficient tensor decomposition, and then recover the low-rank approximation of the current tensor. By randomly selecting additional subspaces, we successfully overcome the issue of local optima at an extremely low computational cost. We evaluate our method on two tasks in online multivariate spatio-temporal analysis: online forecasting and multi-model ensemble. Experiment results show that our method achieves comparable predictive accuracy with significant speed-up.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !