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A new compressed cover tree for k-nearest neighbour search and the stable-under-noise mergegram of a point cloud
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A new compressed cover tree for k-nearest neighbour search and the stable-under-noise mergegram of a point cloud
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This thesis consists of two topics related to computational geometry and one topic related to topological data analysis (TDA), which combines fields of computational geometry and algebraic topology for analyzing data. The first part studies the classical problem of finding k nearest neighbors to m query points in a larger set of n reference points in any metric space. The second part is about the construction of a Minimum Spanning Tree (MST) on any finite metric space. The third part extends the key concept of persistence within Topological Data Analysis in a new direction.
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