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• W2809536859 abstract "Dimensionality reduction is an integral part of data visualization. It is a process that obtains a structure preserving low-dimensional representation of the high-dimensional data. Two common criteria can be used to achieve a dimensionality reduction: distance preservation and topology preservation. Inspired by recent work in topological data analysis, we are on the quest for a dimensionality reduction technique that achieves the criterion of homology preservation, a generalized version of topology preservation. Specifically, we are interested in using topology-inspired manifold landmarking and manifold tearing to aid such a process and evaluate their effectiveness." @default.
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• W2809536859 date "2018-06-21" @default.
• W2809536859 modified "2023-09-27" @default.
• W2809536859 title "Homology-preserving dimensionality reduction via manifold landmarking and tearing" @default.
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