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Author: Masaaki Ida

Affiliation: National Institution for Academic Degrees and Quality Assurance of Higher Education, Kodaira, Tokyo, Japan

Keyword(s): Pattern Recognition, Eigenvalue Distribution, Signal, Randomness, Large Covariance Matrix, Neural Network.

Abstract: Pattern recognition applications and methods are important areas in modern data science. One of the conventional issues for the analysis is the selection of important signal eigenvalues from many eigenvalues dominated by randomness. However, appropriate theoretical reason for selection criteria is not indicated. In this paper, investigating eigenvalue distribution of large covariance matrix for data matrix, comprehensive discrimination method of signal eigenvalues from the bulk of eigenvalues due to randomness is investigated. Applying the discrimination method to weight matrix of three-layered neural network, the method is examined by handwritten character recognition example.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Ida, M. (2024). Discrimination of Signals from Large Covariance Matrix for Pattern Recognition. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 866-871. DOI: 10.5220/0012463300003654

@conference{icpram24,
author={Masaaki Ida.},
title={Discrimination of Signals from Large Covariance Matrix for Pattern Recognition},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={866-871},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012463300003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Discrimination of Signals from Large Covariance Matrix for Pattern Recognition
SN - 978-989-758-684-2
IS - 2184-4313
AU - Ida, M.
PY - 2024
SP - 866
EP - 871
DO - 10.5220/0012463300003654
PB - SciTePress

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