Gromov Wasserstein Framework and Survey
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Xu, Kyle
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Abstract
In biological research, analyzing experimental data for cell type classification is a challenging and time-consuming task. Gromov-Wasserstein optimal transport methods offer promising results, but they face scalability issues with large datasets and variable features. To address this, we propose a Python-based framework facilitating the comparison of Gromov-Wasserstein implementations. We review literature, outline methodology, and assess scalability and accuracy metrics using simulated data. Results highlight Sliced GW's efficiency and Sampled Sliced GW's promise compared to other variations. Our work underscores the need for scalable and accurate computational methods in biological research, with implications for machine learning and bioinformatics.
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Undergraduate Research Option Thesis