paradigm
发表于 2025-3-28 14:35:22
Conclusions,Dimensionality Reduction (DR) enables analysts to perform visual exploration of high dimensional data by providing a low-dimensional representation. On its own, it allows, for instance, to identify at a glance data structures such as clusters, hierarchies or outliers.
indices
发表于 2025-3-28 21:39:57
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cushion
发表于 2025-3-29 02:45:45
Intrinsic Dimensionality,umed to live in a manifold whose dimensionality is lower than that of the data space dimensionality. The effective dimensionality of data is called intrinsic dimensionality. Its estimation is detailed Sect. 2.2.
平庸的人或物
发表于 2025-3-29 05:20:36
Stress Functions for Supervised Dimensionality Reduction,tion for which the desired output must be provided for a training set. Yet, DR is sometimes applied to datasets for which such annotations are available (e.g., class-information). In this context, supervised dimensionality reduction methods seek to take advantage of that information to improve the representation of the data structure.
胰脏
发表于 2025-3-29 09:43:34
Optimization, Acceleration and Out of Sample Extensions, effectively used (Sect. 7.1). It then addresses the issue of time and space complexity and DR techniques acceleration (Sect. 7.2). Finally, it dives into out of sample extension of the mapping, which allows to position new data points a posteriori onto a given map (Sect. 7.3).