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• W2783133864 abstract "As an effective blind source separation method, non-negative matrix factorization has been widely adopted to analyze mixed data in hyperspectral image. However some constraints have to be added in the objective function for more accurate estimates due to the existence of local optima. In this paper, a new NMF-based mixed data analysis algorithm is presented, with maximum overall coverage constraint introduced in traditional NMF, referred to as the MOCC-NMF. Furthermore, in order to handle huge computation involved, parallelism implementation of proposed algorithm using MapReduce is described and the new partitioning strategy to obtain matrix multiplication and determinant value is discussed in detail. In the numerical experiments conducted on real hyperspectral and synthetic datasets of different sizes, the efficiency and scalability of the proposed algorithm is confirmed." @default.
• W2783133864 created "2018-01-26" @default.
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• W2783133864 date "2018-01-01" @default.
• W2783133864 modified "2023-09-27" @default.
• W2783133864 title "Implementation Maximum Overall Coverage Constraint Non-negative Matrix Factorization for Hyperspectral Mixed Pixels Analysis Using MapReduce" @default.
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• W2783133864 doi "https://doi.org/10.1007/978-3-319-73830-7_41" @default.
• W2783133864 hasPublicationYear "2018" @default.
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