Data compression
5 articles · search the full text for this term
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Matrix Factorization and Tensor Decomposition at Scale: Mathematical Foundations and Computational Approaches
Abstract: Matrix factorization and tensor decomposition techniques have emerged as fundamental tools in machine learning and data science for handling high dimensional data efficiently. This paper presents a comprehensive analysis of scalable matrix factorization and tensor decomposition methods, focusing on their mathematical foundations, computational complexity, and practical applications. We examine key algorithms including Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), CP decomposition, and Tucker decomposition, with particular emphasis on their …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 56–59 Read article
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Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing
Abstract: The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 46–62 Read article
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Data Compression for Backbone Network
Abstract: This article involves the application of data compression techniques to improve the efficiency and performance of the core infrastructure of modern digital networks. This approach focuses on reducing the size of transmitted data without compromising its quality, aiming to enhance network throughput, reduce latency, and minimize energy consumption. The study also considers practical implementation challenges and trade-offs to optimize resource utilization in backbone networks. We delve into various compression methods, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 1, 2024 · pp. 30–40 Read article
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Adaptive Huffman Algorithm for Data Compression Using Text Clustering and Multiple Character Modification
Abstract: Adaptive Huffman algorithm is a popular data compression technique that creates a variable-length binary code for each symbol in a message. However, the original algorithm may not be efficient in compressing text data, particularly when dealing with long sequences of repeated characters. In this study, we propose a novel approach to enhance the compression ratio of the Adaptive Huffman algorithm by utilizing text clustering and multiple character modification. The proposed …
Published in Recent Trends in Programming languages · Vol. 10, Issue 1, 2023 · pp. 30–40 Read article
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Development and Performance Evaluation of Efficient Low-Complexity SPIHT Image Coder
Abstract: Wavelet transform is one of the advanced, effective and computationally fast methods for image data as well as video compression. The wavelet based image compression is particularly a nonreversible method that has been growing computationally more complicated as they getting more accurate and reliable. In this work we have developed an image compression technique based on main concepts related to partial ordering of the coefficients of image matrix elements by …
Published in Journal of Advanced Database Management & Systems · Vol. 2, Issue 3, 2015 · pp. 5–10 Read article