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

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Conventional Statistical Approaches,d from the dataset. Methods include Principal Component Analysis (PCA) and its variants, Independent component analysis and Discriminant Analysis. Linear algebra methods offer other approaches, including Singular value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF).

Orchiectomy

Information-Theoretic Approaches,rprisingly interesting reductions. This chapter discusses five major variations of this idea, including comparisons using the concept of mutual information previously used in statistics and machine learning.

豐滿有漂亮

Molecular Computing Approaches, leveraged to render several variations of this theme. They can be used obviously with genomic data, but perhaps surprisingly, with ordinary abiotic data just as well. Two major families of techniques of this kind are reviewed, namely genomic and pmeric coordinate systems for dimensionality reduction and data analysis.

成績上升

Statistical Learning Approaches,et variable based on various statistical solution methods. This chapter describes methods using linear regression and regularization that afford solutions to dimensionality reduction and solutions to problems that are explainable to humans.

成績上升

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Geometric Approaches,called manifold) that can be fitted to the data while trying to minimize the deformations of distances as much as possible. Four major methods of this kind are reviewed, namely MDS, ISOMAP, .-., and random projections.

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