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Multivariate Density Estimation: An SVM Approach

Author(s)
Mukherjee, Sayan; Vapnik, Vladimir
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Abstract
We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.
Date issued
1999-04-01
URI
http://hdl.handle.net/1721.1/7260
Other identifiers
AIM-1653
CBCL-170
Series/Report no.
AIM-1653CBCL-170

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  • AI Memos (1959 - 2004)
  • CBCL Memos (1993 - 2004)

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