Tensor Data Imputation by PARAFAC with updated Chaotic Biases by Adam Optimizer
Pooja Choudhary1, Kanwal Garg2
1Pooja Choudhary, Department of Electronics and Communication, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur (Rajasthan), India.
2Kanwal Garg, Department of Electronics and Communication, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur (Rajasthan), India.
Manuscript received on 23 December 2020 | Revised Manuscript received on 27 December 2020 | Manuscript Accepted on 15 January 2021 | Manuscript published on 30 January 2021 | PP: 18-28 | Volume-1, Issue-1, January 2021 | Retrieval Number: A1004011121/2021©LSP
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The big data pattern analysis suffers from incorrect responses due to missing data entries in the real world. Data collected for digital movie platforms like Netflix and intelligent transportation systems is Spatio-temporal data. Extracting the latent and explicit features from this data is a challenge. We present the high dimensional data imputation problem as a higher-order tensor decomposition. The regularized and biased PARAFAC decomposition is proposed to generate the missing data entries. The biases are created and updated by a chaotic exponential factor in Adam’s optimization, which reduces the imputation error. This chaotic perturbed exponentially update in the learning rate replaces the fixed learning rate in the bias update by Adam optimization. The idea has experimented with Netflix and traffic datasets from Guangzhou, China.
Keywords: Tensor decomposition, PARAFAC, Adam optimization, Data imputation, etc.