Towards Sustainability of AI

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Reduce Data Dimensionality new ​

Overview ​

Sustainability DimensionEcological
ML Development PhaseData Collection and Preparation
ML Development StakeholdersDomain Expert, ML Development

Description ​

“Reduce Data Dimensionality” embraces a set of techniques to reduce the energy impact of data storage and processing by mapping inputs from higher dimensions to lesser dimensions without losing important information (Chhikara et al., 2022; Reddy et al., 2020). Therefore, Yu (2017) suggests assessing the quantity of data required for the desired level of performance. Furthermore, aggregating or dropping attributes can decrease the data amount. Similarly, it might appear reasonable to investigate the effects of larger measuring intervals (for time series data) or smaller sample sizes (for cross-sectional data) to downsize the data (Reddy et al., 2020; Schneider et al., 2019).

Sources ​

  • Chhikara, P., Jain, N., Tekchandani, R., & Kumar, N. (2022). Data dimensionality reduction techniques for Industry 4.0: Research results, challenges, and future research directions. Software: Practice and Experience, 52(3), 658–688. https://doi.org/10.1002/spe.2876
  • Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of Dimensionality Reduction Techniques on Big Data. IEEE Access, 8, 54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942
  • Schneider, J., Basalla, M., & Seidel, S. (2019). Principles of Green Data Mining. Proceedings of the 52nd Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.250