Abstract
Tasks like survey analysis involve collection of large amounts of data from different sources. However, there are several situations where exhaustive data collection could be quite cumbersome or infeasible. In this paper, we propose a novel Compressive Sensing (CS)-based framework to recover the original data from less number of collected data points in the case of market or survey research. We utilize the historical data to establish sparsity of data, and further introduce the concept of logical proximity for better recovery results. Additionally, we also present a conceptual idea toward adaptive sampling using data stream sketching, which suggests whether the collected data measurements are sufficient or not. The proposed CS-based methodology is tested with toy-sized examples and the results are presented to demonstrate its utility.
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Spoorthy, S., Thaskani, S., Sood, A., Chandra, M.G., Balamuralidhar, P. (2016). Missing Data Interpolation Using Compressive Sensing: An Application for Sales Data Gathering. In: Singh, R., Vatsa, M., Majumdar, A., Kumar, A. (eds) Machine Intelligence and Signal Processing. Advances in Intelligent Systems and Computing, vol 390. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2625-3_3
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DOI: https://doi.org/10.1007/978-81-322-2625-3_3
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