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

Ali Emre Pamuk; Yousif Suhail; Fahreddin Şükrü Torun

Makale detayları

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

Yazarlar:

Ali Emre Pamuk | Yousif Suhail | Fahreddin Şükrü Torun

Yayıncı:

Karabuk University

Ana Başlıklar:

Graph | Social and Multimedia Data | Data Engineering and Data Science

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.58-74

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350065

pISSN:

2980-3152

Anahtar kelimeler:

Özet:

 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.

 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.

Makale detayları

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

Yazarlar:

Ali Emre Pamuk | Yousif Suhail | Fahreddin Şükrü Torun

Yayıncı:

Karabuk University

Ana Başlıklar:

Graph | Social and Multimedia Data | Data Engineering and Data Science

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.58-74

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350065

pISSN:

2980-3152

Sparse matrices

matrix generator

matrix scaling

data augmentation

signal processing

image processing

Sayı

Makale detayları

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

Yazarlar: Ali Emre Pamuk | Yousif Suhail | Fahreddin Şükrü Torun

Yayıncı: Karabuk University

Ana Başlıklar: Graph | Social and Multimedia Data | Data Engineering and Data Science

Dergi: Current Trends in Computing Vol.3 Issue 1, pp.58-74

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001350065

pISSN: 2980-3152

Sparse matrices

matrix generator

matrix scaling

data augmentation

signal processing

image processing

Sayı

Yazarlar:

Ali Emre Pamuk | Yousif Suhail | Fahreddin Şükrü Torun

Yayıncı:

Karabuk University

Ana Başlıklar:

Graph | Social and Multimedia Data | Data Engineering and Data Science

Dergi:

Current Trends in Computing Vol.3 Issue 1, pp.58-74

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350065

pISSN:

2980-3152