主要参考的是Kaggle上的一篇Kernel,也可以直接去看这篇Kernel,这里附上链接:Interactive Intro to Dimensionality Reduction <https://www.kaggle.com/arthurtok/interactive-intro-to-dimensionality-reduction> python代码: 导入一些基本的库 import numpy as np # linear algebra import pandas as pd # data ...
SVD is based on a theorem from linear algebra which says that a rectangular matrix M can be split down into the product of three new matrices: An orthogonal matrix U; A diagonal matrix S; The transpose of an orthogonal matrix V. Usually the theorem is written as follows: Mmn=UmmSmn...
rmd_files: ["index.Rmd", "01-getting-started-R.Rmd", "02-intro-R-markdown.Rmd", "03-data-structures.Rmd", "07-functions-and-control-flow.Rmd", "04-ggplot-intro.Rmd", "05-dplyr-intro.Rmd", "06-data-wrangling.Rmd", "08-vector-algebra.Rmd", "09-matrices.Rmd", "10-linear-reg...
SVD is based on a theorem from linear algebra which says that a rectangular matrix M can be split down into the product of three new matrices: An orthogonal matrix U; A diagonal matrix S; The transpose of an orthogonal matrix V. Usually the theorem is written as follows: Mmn=UmmSmn...