Titlebook: Dimensionality Reduction in Data Science; Max Garzon,Ching-Chi Yang,Lih-Yuan Deng Book 2022 The Editor(s) (if applicable) and The Author(s

沙发

连锁,连串

What Is Dimensionality Reduction (DR)?,ity to generate, gather, and store volumes of data (order of tera- and exo-bytes, 10. − 10. daily) has far outpaced our ability to derive useful information from it in many fields, with available computational resources. Therefore, data reduction is a critical step in order to turn large datasets in

爆炸

Conventional Statistical Approaches,space. Statistical methods aim to preserve characteristic parameters such as mean, variance, and covariance of features in the population, as estimated from the dataset. Methods include Principal Component Analysis (PCA) and its variants, Independent component analysis and Discriminant Analysis. Lin

Apraxia

Geometric Approaches,r of features. After the classical PCA that fits a linear (flat) subspace so that the total sum of squared distances of the data from the subspace (errors) is minimized, any distance function in this space can be used to endow it with a geometric structure, where ordinary intuition can be particular

Left-Atrium

febrile