Tuning Database-Friendly Random Projection Matrices for Improved Distance Preservation on Specific Data
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10489_2021_Article_2626.pdf
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Author(s)
López-Sánchez, Daniel
de Bodt, Cyril
Lee, John A.
Arrieta, Angélica G.
Corchado, Juan M.
Date Issued
July 2021
Journal
Applied Intelligence
Publisher
Springer US
Citation
López-Sánchez, D., de Bodt, C., Lee, J.A. et al. Tuning Database-Friendly Random Projection Matrices for Improved Distance Preservation on Specific Data. Appl Intell (2021)
Version
Final published version
Abstract
Random Projection is one of the most popular and successful dimensionality reduction algorithms for large volumes of data. However, given its stochastic nature, different initializations of the projection matrix can lead to very different levels of performance. This paper presents a guided random search algorithm to mitigate this problem. The proposed method uses a small number of training data samples to iteratively adjust a projection matrix, improving its performance on similarly distributed data. Experimental results show that projection matrices generated with the proposed method result in a better preservation of distances between data samples. Conveniently, this is achieved while preserving the database-friendliness of the projection matrix, as it remains sparse and comprised exclusively of integers after being tuned with our algorithm. Moreover, running the proposed algorithm on a consumer-grade CPU requires only a few seconds.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10489-021-02626-6
