Online Kernel Density Estimation for learning and classification

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• Dr. Matej Kristan, Visual Cognitive Systems Laboratory, Faculty of Computer and Information Science, University of Ljubljana
• Friday 17 May 2013, 16:00-17:00
• UG09, Learning Centre.

If you have a question about this talk, please contact a.Leonardis.

Host: Prof. Ales Leonardis

Please note that coffee will be served at 3:15 at the coffee room (School of Computer Science), followed by the one-hour seminar at 4pm.

Many practical applications require building probabilistic models of a perceived environment, or of an observed process, in form of probability density functions (pdf) over some moderate-dimensional feature space. Building the pdf models from large batches of data may be computationally infeasible. Moreover, all the data may not be available in advance. We might want to observe some process for an indefinite duration, while continually providing the best estimate of the pdf from the data observed so far. To guarantee the model’s low computational complexity at the its application, the model should remain simple enough even after observing many new data-points.

In this talk I will present an approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The approach is called the online Kernel Density Estimation (oKDE) and generates a Gaussian mixture model from a stream of data-points. It retains the nonparametric quality of a KDE , allows adaptation from as little as a single data-point at a time, while keeping the model’s complexity low. The oKDE allows estimation of stationary as well as nonstationary distributions and is extendable to online construction of classifiers. I will show the main results of oKDE comparison to competing batch KDEs and support vector machines on problems of online estimation of generative models as well as classifiers.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• UG09, Learning Centre
• computer sience

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