A Deep Learning Framework for HighDimensional Data Analytics
Year: 2025
Type: article
Abstract: High-dimensional data analysis has become an essential area of focus across various fields, including healthcare, finance, cybersecurity, and scientific research. However, this type of data has its own challenges of dimensionality, which leads to exponential growth in computational complexity as dimensions increase. A key strength of Deep Learning (DL) is its ability to automatically learn complex patterns and feature representations, making it a powerful alternative for such problems. This paper is structured to introduce a deep learning framework designed to address the challenges faced in high-dimensional data analytics and state-of-the-art solutions. We explore techniques such as Dimensionality Reduction, Feature Extraction, Optimization Strategies, and Scalability.
Authors: Staff Engineer, Neiman Marcus, Texas, USA, Hanza Parayil Salim
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408036322