Puchała, Dariusz2023-09-252023-09-252023Puchała D., Local Energy Redistribution Units for Space Dimensionality Reduction in Data Classification. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 355-360, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.56.978-83-66741-92-8http://hdl.handle.net/11652/4832https://doi.org/10.34658/9788366741928.56In this paper, we present locally trained 2-input to 2-output neurons called Local Energy Redistribution Units (LERUs), which enable to transfer most of the input data energy to the selected output, and when organized into properly designed networks, allow for the energy accumulation in lower-indexed elements of output vectors. This property can be used to reduce the dimensionality of the input data space, resulting in a reduction in the number of weights and disk space needed to store neural network models. We test the effectiveness of the proposed approach experimentally in the task of data classification using the well-known MNIST dataset.enDla wszystkich w zakresie dozwolonego użytkuFair use conditionlocally trained neuronscompression of neural networksneurony wytrenowane lokalniekompresja sieci neuronowychRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.56