Asymptotically optimal approximation of multidimensional pdf's by lower dimensional pdf's
Document Type
Article
Date of Original Version
2-1-2007
Abstract
Probability density functions (pdf's) of high dimensionality are impractical to estimate from real data. For accurate estimation, the dimensionality of the pdf can be at most 5-10. In order to reduce the dimensionality a sufficient statistic is usually employed. When none is available, there is no universal agreement on how to proceed. We show how to construct a high-dimension pdf based on the pdf of a low-dimensional statistic that is closest to the true one in the sense of divergence. The latter criterion asymptotically minimizes the probability of error in a decision rule. An application to feature selection for classification is described. © 2006 IEEE.
Publication Title, e.g., Journal
IEEE Transactions on Signal Processing
Volume
55
Issue
2
Citation/Publisher Attribution
Kay, Steven. "Asymptotically optimal approximation of multidimensional pdf's by lower dimensional pdf's." IEEE Transactions on Signal Processing 55, 2 (2007): 725-729. doi: 10.1109/TSP.2006.887112.