SNEER
发表于 2025-3-26 22:19:22
Sufficient Dimension Reduction and Kernel Dimension ReductionSuppose there is a dataset that has labels, either for regression or classification. Sufficient Dimension Reduction (SDR), first proposed by Li, is a family of methods that find a transformation of the data to a lower dimensional space, which does not change the conditional of labels given the data.
免费
发表于 2025-3-27 04:17:13
http://reply.papertrans.cn/31/3076/307583/307583_32.png
MAIZE
发表于 2025-3-27 05:36:28
978-3-031-10604-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
CRASS
发表于 2025-3-27 11:10:45
Benyamin Ghojogh,Mark Crowley,Ali GhodsiExplains the theory of fundamental algorithms in dimensionality reduction, in a step-by-step and very detailed approach.Useful for anyone who wants to understand the ways to extract, transform, and un
Extricate
发表于 2025-3-27 15:06:46
http://image.papertrans.cn/e/image/307583.jpg
机警
发表于 2025-3-27 21:33:14
http://reply.papertrans.cn/31/3076/307583/307583_36.png
皱痕
发表于 2025-3-27 23:52:17
http://reply.papertrans.cn/31/3076/307583/307583_37.png
Redundant
发表于 2025-3-28 04:13:35
http://reply.papertrans.cn/31/3076/307583/307583_38.png
同音
发表于 2025-3-28 06:36:50
,L’adolescent, la mère et l’enfant,cipal Component Analysis (PCA) (see Chap. .) and Fisher Discriminant Analysis (FDA) (see Chap. .), learn a projection matrix for either better representation of data or discrimination between the classes in the subspace.
公理
发表于 2025-3-28 11:05:14
https://doi.org/10.1007/978-3-031-10602-6Data Reduction; Data Visualization; Dimensionality Reduction; Feature Extraction; Machine Learning; Manif