dor_id: 41599

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856.4.0.u: https://rmf.smf.mx/ojs/rmf/article/view/3743/3710

100.1.#.a: Cruz, B.; Barrón, R.; Sossa.

524.#.#.a: Cruz, B., et al. (2010). Geometric associative memories applied to pattern restoration. Revista Mexicana de Física; Vol 56, No 2: 155-0. Recuperado de https://repositorio.unam.mx/contenidos/41599

245.1.0.a: Geometric associative memories applied to pattern restoration

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Facultad de Ciencias, UNAM

264.#.0.c: 2010

264.#.1.c: 2010-01-01

653.#.#.a: Associative memories; pattern restoration; mixed noise; conformal geometric algebra

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, fecha de asignación de la licencia 2010-01-01, para un uso diferente consultar al responsable jurídico del repositorio por medio de rmf@ciencias.unam.mx

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001.#.#.#: oai:ojs.rmf.smf.mx:article/3743

041.#.7.h: eng

520.3.#.a: Two main research areas in Pattern Recognition are pattern classification and pattern restoration. In the literature, many models have been developed to solve many of the problems related to these areas. Among these models, Associative Memories (AMs) can be highlighted. An AM can be seen as a one-layer Neural Network. Recently, a Geometric Algebra based AM model was developed for pattern classification, the so-called Geometric Associative Memories (GAMs). In general, AMs are very efficient for restoring patterns affected BY either additive or subtractive noise, but in the case of mixed noise their efficiency is very poor. In this work, modified GAMs are used to solve the problem of pattern restoration. This new modification makes use of Conformal Geometric Algebra principles and optimization techniques to completely and directly restore patterns affected by (mixed) noise. Numerical and real examples are presented to test whether the modification can be efficiently used for pattern restoration. The proposal is compared with other reported approaches in the literature. Formal conditions are also given to ensure the correct functioning of the proposal.

773.1.#.t: Revista Mexicana de Física; Vol 56, No 2 (2010): 155-0

773.1.#.o: https://rmf.smf.mx/ojs/rmf/index

046.#.#.j: 2020-11-25 00:00:00.000000

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handle: 00b3544ef04f11a4

harvesting_date: 2020-09-23 00:00:00.0

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last_modified: 2020-11-27 00:00:00

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Artículo

Geometric associative memories applied to pattern restoration

Cruz, B.; Barrón, R.; Sossa.

Facultad de Ciencias, UNAM, publicado en Revista Mexicana de Física, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Facultad de Ciencias, UNAM
Revista
Repositorio
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Cruz, B., et al. (2010). Geometric associative memories applied to pattern restoration. Revista Mexicana de Física; Vol 56, No 2: 155-0. Recuperado de https://repositorio.unam.mx/contenidos/41599

Descripción del recurso

Autor(es)
Cruz, B.; Barrón, R.; Sossa.
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Geometric associative memories applied to pattern restoration
Fecha
2010-01-01
Resumen
Two main research areas in Pattern Recognition are pattern classification and pattern restoration. In the literature, many models have been developed to solve many of the problems related to these areas. Among these models, Associative Memories (AMs) can be highlighted. An AM can be seen as a one-layer Neural Network. Recently, a Geometric Algebra based AM model was developed for pattern classification, the so-called Geometric Associative Memories (GAMs). In general, AMs are very efficient for restoring patterns affected BY either additive or subtractive noise, but in the case of mixed noise their efficiency is very poor. In this work, modified GAMs are used to solve the problem of pattern restoration. This new modification makes use of Conformal Geometric Algebra principles and optimization techniques to completely and directly restore patterns affected by (mixed) noise. Numerical and real examples are presented to test whether the modification can be efficiently used for pattern restoration. The proposal is compared with other reported approaches in the literature. Formal conditions are also given to ensure the correct functioning of the proposal.
Tema
Associative memories; pattern restoration; mixed noise; conformal geometric algebra
Idioma
eng
ISSN
2683-2224 (digital); 0035-001X (impresa)

Enlaces