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- W2896033033 abstract "To achieve efficient component pursuit from noisy measurements, a learning algorithm is presented that combines standard gradient principle and the standard stochastic approximations. By extending the linear predictor principle from noise-free case, a proper objective function is introduced which has the same generic form as that for the noise-free case. Extensive computer simulations are performed to illustrate the power of the presented technique." @default.
- W2896033033 created "2018-10-26" @default.
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- W2896033033 date "2018-06-01" @default.
- W2896033033 modified "2023-09-27" @default.
- W2896033033 title "Component Pursuit from Noisy Measurements" @default.
- W2896033033 doi "https://doi.org/10.1109/icsgea.2018.00044" @default.
- W2896033033 hasPublicationYear "2018" @default.
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