1. bookVolumen 22 (2022): Edición 2 (June 2022)
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Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
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4 veces al año
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access type Acceso abierto

Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Publicado en línea: 23 Jun 2022
Volumen & Edición: Volumen 22 (2022) - Edición 2 (June 2022)
Páginas: 36 - 49
Recibido: 01 Mar 2022
Aceptado: 29 Mar 2022
Detalles de la revista
License
Formato
Revista
eISSN
1314-4081
Primera edición
13 Mar 2012
Calendario de la edición
4 veces al año
Idiomas
Inglés
Abstract

Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when the size of data grows large it becomes slower. In this paper, a modified version of LVQ, which is called PDLVQ is proposed to accelerate the traditional version. The proposed scheme aims to avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are used in the experiments. The comparisons have been done between LVQ and PDLVQ in terms of runtime and in result, it turns out that PDLVQ shows better efficiency than LVQ. PDLVQ has achieved up to 37% efficiency in runtime compared to LVQ when the dimensions have increased. Also, the enhanced algorithm (PDLVQ) shows clear enhancement to decrease runtime when the size of dimensions, the number of clusters, or the size of data becomes increased compared with the traditional one which is LVQ.

Keywords

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