Deep Learning Approaches for High-Dimensional Data Classification

Abstract

Traditional classification methods have been confronted with major hurdles as a result of the rapid increase in high-dimensional data created across a variety of areas, including bioinformatics, image processing, text analytics, and sensor networks. It is common for high-dimensional datasets to have problems such as the curse of dimensionality, feature redundancy, sparsity, and increasing computing complexity, all of which can have a negative impact on the performance of mathematical models. Because of its capacity to automatically learn hierarchical feature representations from big and complicated datasets, deep learning has emerged as a powerful strategy for high-dimensional data categorization. This is owing to the fact that it can automatically learn these representations. The categorization of high-dimensional data can be accomplished using a variety of deep learning techniques, such as deep neural networks, convolutional neural networks, recurrent neural networks, and autoencoder-based models. The research investigates the ways in which these designs tackle the problem of dimensionality by utilizing feature learning, representation compression, and nonlinear transformations. The performance of the model, its scalability, and its robustness are compared over a variety of data types, and comparative recommendations are made.