CMU VASC talk: Learning to Transform Time Series with a Few Examples

Ali Rahimi, Intel Lab Seattle
Monday, Feb 27, 2006

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 algorithm 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 memoryless transformations of time series from a few example input-output mappings. Our 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 algorithm 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.


Speaker Bio:
Ali Rahimi is interested in developing machine learning tools for solving difficult sensing problems. His focus is on example-based tracking, and efficient approximation methods for estimation. He received a PhD from the MIT Computer Science and AI Lab in 2005, a MS in Media Arts and Science from the MIT Media Lab, and a BS in Electrical Engineering and Computer Science from UC Berkeley.