2026-09-15T23:38:55Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1497912025-04-30T20:36:46Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243

López Sánchez, Daniel Bodt, Cyril de Lee, John A. González Arrieta, María Angélica Corchado Rodríguez, Juan Manuel 2022 [EN] 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. http://hdl.handle.net/10366/149791 eng Springerlink 1203.02 Lenguajes Algorítmicos 1206.10 Matrices Tuning Database-Friendly Random Projection Matrices for Improved Distance Preservation on Specific Data info:eu-repo/semantics/article TEXT Gredos. Repositorio Documental de la Universidad de Salamanca Hispana