Proceedings Paper
Earth mover's distances of feature vectors in large data analyses| Format | Member Price | Non-Member Price |
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Paper 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.
Paper Details
Date Published: 20 May 2015
PDF: 8 pages
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
Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)
PDF: 8 pages
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
Show Author Affiliations
Anurag Singh, Univ. of Louisiana at Lafayette (United States)
Henry Chu, Univ. of Louisiana at Lafayette (United States)
Henry Chu, Univ. of Louisiana at Lafayette (United States)
Michael Pratt, Univ. of Louisiana at Lafayette (United States)
Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)
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