rahimi-thesis - Learning to Transform Time Series with a...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Learning to Transform Time Series with a Few Examples by Ali Rahimi Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Electrical Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY Feb 2005 c Massachusetts Institute of Technology 2005. All rights reserved. Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Department of Electrical Engineering and Computer Science 4 Nov 2005 Certified by. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trevor J. Darrell Associate Professor Thesis Supervisor Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arthur C. Smith Chairman, Department Committee on Graduate Students Learning to Transform Time Series with a Few Examples by Ali Rahimi Submitted to the Department of Electrical Engineering and Computer Science on 4 Nov 2005, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science and Electrical Engineering Abstract I describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. I apply this al- gorithm to tracking, where one transforms a time series of observations from sensors to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, I suggest learning a memory- less transformations of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. I relate this algorithm and its unsupervised extension to nonlinear system identification and manifold learning techniques. I demonstrate it on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences, and tracking a target in a completely uncalibrated network of sensors. For these tasks, this algo- rithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account. Thesis Supervisor: Trevor J. Darrell Title: Associate Professor 2 Acknowledgments This thesis is the result of a collaboration with Ben Recht. It is the culmination of many brainstorming sessions and a few papers we coauthored. I thank him for the most fruitfuil collaboration I have ever had....
View Full Document

This note was uploaded on 09/21/2010 for the course CS 5800 taught by Professor Reddy during the Fall '10 term at Wayne State University.

Page1 / 120

rahimi-thesis - Learning to Transform Time Series with a...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online