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- W3199424861 abstract "Big data sets are characterized by raw data with large dimension, noise accumulation, spurious correlation and heavy-tailed behavior along with measurement outliers. Therefore, a unified approach is inevitable to preprocess the raw data in order to improve the prediction accuracy. In this paper, we present updated data preprocessing framework based on adaptive filtering algorithm with data selection capability together with removal of outliers and noise. The l1 norm minimization along with a new update rule based on higher order statistics of error enables dimension reduction without any sacrifice in accuracy. Thus, the proposed zero attraction data selective least mean square (ZA-DS-LMS) algorithm can claim for the reduction in the computational cost with the improvement in convergence speed and steady-state mean square error (MSE) without any compromise in the accuracy. Simulations were performed in real and simulated big data to validate the performance improvement of the proposed algorithm in the context of preprocessing for big data analysis." @default.
- W3199424861 created "2021-09-27" @default.
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- W3199424861 date "2021-07-07" @default.
- W3199424861 modified "2023-10-18" @default.
- W3199424861 title "Zero attraction data selective adaptive filtering algorithm for big data applications" @default.
- W3199424861 doi "https://doi.org/10.1049/pbpc037g_ch2" @default.
- W3199424861 hasPublicationYear "2021" @default.
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