20 May 2015 Earth mover's distances of feature vectors in large data analyses
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Abstract
The earth mover's distance (EMD) measures the difference of two feature vectors that is related to the Wasserstein metric defined for probability distribution functions on a given metric space. The EMD of two vectors is based on the cost of moving the content of individual elements of an anchor vector to match the distribution of a target vector. The EMD is a solution to a transportation problem. We present results of using EMD in large data analysis problems such as those for health data and image data.
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Anurag Singh, Anurag Singh, Henry Chu, Henry Chu, Michael Pratt, Michael Pratt, } "Earth mover's distances of feature vectors in large data analyses", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960D (20 May 2015); doi: 10.1117/12.2180707; https://doi.org/10.1117/12.2180707
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