Explore >> Select a destination
|
You are here 
|
nhigham.com | ||
| | | | |
blog.autarkaw.com
|
|
| | | | | ||
| | | | |
blog.autarkaw.com
|
|
| | | | | ||
| | | | |
blog.georgeshakan.com
|
|
| | | | | Principal Component Analysis (PCA) is a popular technique in machine learning for dimension reduction. It can be derived from Singular Value Decomposition (SVD) which we will discuss in this post. We will cover the math, an example in python, and finally some intuition. The Math SVD asserts that any $latex m \times d$ matrix $latex... | |
| | | | |
arkadiusz-jadczyk.eu
|
|
| | | In the last post, Geodesics of left invariant metrics on matrix Lie groups - Part 1,we have derived Arnold's equation - that is a half of the problem of finding geodesics on a Lie group endowed with left-invariant metric. Suppose $G$ is a Lie group, and $g(\xi,\eta)$ is a scalar product (i.e. | ||
