MATGEN: A REALISTIC SPARSE MATRIX GENERATOR USING SIGNAL PROCESSING AND IMAGE PROCESSING METHODS

The limited size of publicly available sparse matrix datasets creates a significant challenge for benchmarking, testing, and validating algorithms in scientific computing, artificial intelligence and other data-intensive applications. Existing approaches such as random matrix generators or general d...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Karabuk University - Computer Engineering and Software Engineering Departments
Hlavní autoři: PAMUK, ALI EMRE, KAPLAN, FARUK, SUHAIL, YOUSIF, ALTEKIN, MERT, TORUN, FAHREDDIN SUKRU
Médium: Journal Article
Jazyk:angličtina
Vydáno: 30.06.2025
ISSN:2980-3152, 2980-3152
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The limited size of publicly available sparse matrix datasets creates a significant challenge for benchmarking, testing, and validating algorithms in scientific computing, artificial intelligence and other data-intensive applications. Existing approaches such as random matrix generators or general data augmentation methods often fail to produce structurally realistic matrices. To address this gap, we present MatGen which a tool for generating realistic variations of a given sparse matrix using signal processing and image processing techniques. MatGen takes a real sparse matrix as input and produces structurally consistent matrices at different sizes, introducing controlled variation while preserving key sparsity patterns. We evaluate the effectiveness of MatGen by analyzing structural features and visual similarities between original and generated matrices. Experimental results show that MatGen can produce realistic, scalable sparse matrices suitable for a wide range of applications including benchmarking computational methods, and sparse data techniques. The limited size of publicly available sparse matrix datasets creates a significant challenge for benchmarking, testing, and validating algorithms in scientific computing, artificial intelligence and other data-intensive applications. Existing approaches such as random matrix generators or general data augmentation methods often fail to produce structurally realistic matrices. To address this gap, we present MatGen which a tool for generating realistic variations of a given sparse matrix using signal processing and image processing techniques. MatGen takes a real sparse matrix as input and produces structurally consistent matrices at different sizes, introducing controlled variation while preserving key sparsity patterns. We evaluate the effectiveness of MatGen by analyzing structural features and visual similarities between original and generated matrices. Experimental results show that MatGen can produce realistic, scalable sparse matrices suitable for a wide range of applications including benchmarking computational methods, and sparse data techniques.
ISSN:2980-3152
2980-3152
DOI:10.71074/CTC.1716528