RandPro- A practical implementation of random projection-based feature extraction for high dimensional multivariate data analysis in R by R. Siddharth, G. Aghila

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Randomized Projection Learning Method for Dynamic Mode Decomposition by Sudam Surasinghe, Erik M. Bollt

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Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods by Michael C. Thrun, PhD, Alfred Ultsch, Prof. Dr. habil.

“…However, the Johnson–Lindenstrauss lemma states that the two-dimensional similarities in the scatter plot cannot coercively represent high-dimensional structures. …”
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Limitations on quantum dimensionality reduction by Harrow, Aram W., Montanaro, Ashley, Short, Anthony J.

“…The Johnson–Lindenstrauss Lemma is a classic result which implies that any set of n real vectors can be compressed to O(log n) dimensions while only distorting pairwise Euclidean distances by a constant factor. …”
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Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information by Wei Wang, Jianming Wang, Jianhua Chen

“…According to the Johnson–Lindenstrauss lemma, we can use the initial measurement results to perform saliency detection and then obtain the saliency value for each block. …”
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Sketching and streaming high-dimensional vectors by Nelson, Jelani (Jelani Osei)

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