Technical Report Available

[Mark Girolami]

The following new technical report is available at the  website below.
Included on the website are Matlab demos along with all code and data
required to allow easy replication of the experimental results
reported.
 
http://cis.paisley.ac.uk/giro-ci0/reddens/
 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Probability Density Estimation from Optimally Condensed Data Samples.

Mark Girolami & Chao He 
Computing & Information Systems Technical Reports, ISSN-1461-6122. 

  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(N2) 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(N3) 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.   
 
Professor. M.A Girolami PhD
Associate Head of School 
and
Chair of Applied Computational Intelligence
 
School of Information and Communication Technologies
University of Paisley
High Street
Paisley, PA1 2BE
 
Tel: +44 (0)141 848 3317
Fax +44 (0)141 848 3542
 
http://cis.paisley.ac.uk/giro-ci0


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