# How to Reduce Dimension With PCA and Random Projections?

@article{Yang2021HowTR,
title={How to Reduce Dimension With PCA and Random Projections?},
author={Fan Yang and Sifan Liu and E. Dobriban and David P. Woodruff},
journal={IEEE Transactions on Information Theory},
year={2021},
volume={67},
pages={8154-8189}
}
• Fan Yang, +1 author David P. Woodruff
• Published 1 May 2020
• Computer Science, Mathematics
• IEEE Transactions on Information Theory
In our “big data” age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are “data-oblivious” methods such as random projections and sketching, and “data-aware” methods such as principal component analysis (PCA). Both have their strengths, such as speed for random projections, and data-adaptivity for PCA. In this work, we study how to combine them to get the best of both. We study… Expand
10 Citations

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