Theoretical and Computational Advances in Deep Learning for High Dimensional Data Representation and Feature Extraction in Sparse Environments
Keywords:
Deep learning, High-dimensional data, Sparse representation, Feature extraction, Autoencoders, Manifold learning, RegularizationAbstract
Deep learning has revolutionized high-dimensional data representation and feature extraction, particularly in sparse environments where data is incomplete or scattered. This paper reviews theoretical and computational advancements in deep learning models tailored for high-dimensional data, emphasizing techniques such as autoencoders, manifold learning, and sparse coding. The study also discusses optimization strategies and regularization techniques that enhance model performance. Through an analysis of pre 2023 literature, we explore how deep learning mitigates sparsity challenges, focusing on applications in computer vision, natural language processing, and scientific computing. Empirical results using synthetic and real-world datasets highlight the efficiency of modern architectures. Future directions include interpretability improvements and efficient hardware implementation.
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