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In this project, we applied the deep learning methods of autoencoder-Kalman filtering as well as the autoencoder preprocessing from Ciecierski  to improve clustering on action potentials by filtering noise. We used multiple types of clustering, including k-means clustering, mean-shift clustering, and agglomerative hierarchical clustering. We evaluated the performance of each clustering algorithm after using each filtering method, as well as no filtering, to analyze which of the filtering methods has the best effect on clustering action potentials.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
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