Browsing by Author "Pehlivan, Berke"

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    Master Thesis
    Ölçeklenebilir Manifold Öğrenme Kütüphanesi Geliştirilmesi: Scaman
    (2024) Pehlivan, Berke; Yetkin, Emrullah Fatih
    This thesis presents an exploration of manifold learning and dimensionality reduction techniques, which are crucial in the fields of data science and machine learning. The center of this study is the development and evaluation of 'Scaman (Scalable Manifold Library), a Python-based computational tool designed to implement these techniques. This thesis investigates the key manifold learning algorithms. Including PCA,MDS, LE, and LLE and emphasizing the importance of eigenvalue solvers in these algorithms. The contribution of this thesis is the integration of advanced eigensolvers like NumPy, SLEPc and FEAST into key manifold algorithms within scaman package. The empirical analysis was conducted using various synthetic and real-world datasets. Those analyses focused on the efficiency, accuracy, and practical utility of scaman in different scenarios. Results demonstrate the tool's effectiveness, especially in handling large datasets. The advantages of FLANN and SLEPc prove scaman's efficiency in the creation of adjacency matrices and eigenvalue computation. The outcome of this thesis provides a computational tool for researchers and practitioners. Future directions include expanding the tool's capabilities by adding more algorithms, improving scalability, and applying various domain specific data-driven scenarios.