Paper Available : Sparse Kernel Density Estimation

[Mark Girolami]

Probability Density Estimation from Optimally Condensed Data Samples.
IEEE Transactions on Pattern Analysis & Machine Intelligence, 2003, in
press.

Mark Girolami & Chao He. 

Paper and MATLAB Code Available from:
http://cis.paisley.ac.uk/giro-ci0/reddens/
    
Abstract  
The requirement to reduce the computational cost of evaluating a point
probability density estimate when employing a Parzen window estimator is
a well known problem. This paper presents the Reduced Set Density
Estimator that provides a kernel based density estimator which employs a
small percentage of the available data sample and is optimal in the L2
sense. Whilst only requiring O(N^2) optimisation routines to estimate
the required weighting coefficients, the proposed method provides
similar levels of performance accuracy and sparseness of representation
as Support Vector Machine density estimation, which requires O(N^3)
optimisation routines, and which has previously been shown to
consistently outperform Gaussian Mixture Models. It is also demonstrated
that the proposed density estimator consistently provides superior
density estimates for similar levels of data reduction to that provided
by the recently proposed Density Based Multiscale Data Condensation
algorithm and in addition has comparable computational scaling. The
additional advantage of the proposed method is that no extra free
parameters are introduced such as regularisation, bin width or
condensation ratios making this method a very simple and straightforward
approach to providing a reduced set density estimator with comparable
accuracy to that of the full sample Parzen density estimator.
 




Legal disclaimer
--------------------------

The information transmitted is the property of the University of
Paisley and is intended only for the person or entity to which it is
addressed and may contain confidential and/or privileged material.
Statements and opinions expressed in this e-mail may not represent
those of the company.  Any review, retransmission, dissemination and
other use of, or taking of any action in reliance upon, this
information by persons or entities other than the intended recipient
is prohibited.  If you received this in error, please contact the
sender immediately and delete the material from any computer.

--------------------------