Sciweavers

ICASSP
2010
IEEE

Exponentially embedded families for multimodal sensor processing

13 years 11 months ago
Exponentially embedded families for multimodal sensor processing
The exponential embedding of two or more probability density functions (PDFs) is proposed for multimodal sensor processing. It approximates the unknown PDF by exponentially embedding the known PDFs. Such embedding is of a exponential family indexed by some parameters, and hence inherits many nice properties of the exponential family. It is shown that the approximated PDF is asymptotically the one that is the closest to the unknown PDF in Kullback-Leibler (KL) divergence. Applied to hypothesis testing, this approach shows improved performance compared to existing methods for cases of practical importance where the sensor outputs are not independent.
Steven Kay, Quan Ding
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Steven Kay, Quan Ding
Comments (0)