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Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph
2020
Entropy
Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits
doi:10.3390/e22030296
pmid:33286070
pmcid:PMC7516753
fatcat:kdqf5e5zifgzzftgk4vkzsqqli