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• W333102599 abstract "Abstract : The overall objective of this project is to develop transformative theory and algorithms for robust Automated Target Recognition (ATR). This project addressed the following challenging problems in ATR: modeling uncertainty, small sample size, high dimensional data, irrelevant features/dimensions, heterogeneous data, and outliers. In this project, the PI proposed and developed the following new techniques: 1) kernel local feature extraction (KLFE) for ATR applications, 2) technique for identifying network dynamics under sparsity and stationarity constraints, 3) self-organized-queue-based (SOQ) clustering scheme, 4) robust principal component analysis (RPCA) based on manifold optimization, outlier detection, and subspace decomposition." @default.
• W333102599 created "2016-06-24" @default.
• W333102599 creator A5001469325 @default.
• W333102599 date "2012-02-29" @default.
• W333102599 modified "2023-09-23" @default.
• W333102599 title "Joint Information Theoretic and Differential Geometrical Approach for Robust Automated Target Recognition" @default.
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• W333102599 doi "https://doi.org/10.21236/ada564135" @default.
• W333102599 hasPublicationYear "2012" @default.
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