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This paper addresses the problem of robust classification of mixture densities by using an entropic-graph information divergence estimate; this provides a means to robustly estimate I-divergence without using any explicit probability density function estimation procedure. We previously applied entropic-graph methods to clustering and classification for mixture densities having uniform contamination...
In image reconstruction and restoration, there exists an inherent tradeoff between the recovered spatial resolution and statistical variance: lower variance can be bought at the price of decreased spatial resolution. This tradeoff can be captured for a particular regularized estimator by tracing out the resolution and variance as a curve indexed by the estimator's smoothing parameter. When the resolution...
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