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Gradient Surface Clustering for Cost Function Approximation

Explore Deepest Descent and Jensen’s Current Approach to pick gradient surfaces with the smallest Euclidean distance, cluster similar surfaces, & approximate cost functions using EM algorithms. Discover new approaches to cluster gradient surfaces efficiently with Expectation-Maximization for each pixel. Work on an upper-bound cost estimation using Jensen’s Inequality is in progress.

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Gradient Surface Clustering for Cost Function Approximation

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  1. UCF REU Week 8 Lam Tran

  2. Deepest Descent and Jensen’s

  3. Current Approach • Pick gradient surface with the smallest distance in Euclidean space.

  4. New Approach • Cluster Similar Gradient Surfaces • Jensen’s approximation cost function with EM algorithms.

  5. New Approach II Get gradient surfaces for each pixel on penny (done) Cluster gradient surfaces with Expectation-Maximization (EM) for each pixel (done) Upper bound cost with Jensen’s Inequality (in progress)

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