What is dimensionality reduction (dr)?
Abstract
Solutions to problems require either assumptions on the target population or lots of data to train models that may help answer the questions. Our ability to generate, gather, and store volumes of data (order of tera-and exo-bytes, 1012-1018 daily) has far outpaced our ability to derive useful information from it in many fields, with available computational resources. Therefore, data reduction is a critical step in order to turn large datasets into useful information, the overarching purpose of data science. DR thus becomes absolutely essential in DS, particularly for big data.
Publication Title
Dimensionality Reduction in Data Science
Recommended Citation
Deng, L., Garzon, M., & Kumar, N. (2022). What is dimensionality reduction (dr)?. Dimensionality Reduction in Data Science, 67-77. https://doi.org/10.1007/978-3-031-05371-9_3
