And now the time in Adelaide and Perth is over. We are back in London, having arrived on the same day as we left Australia. This was the first time I have done this, and I really don’t recommend it. 540 more words

## Tags » Eigenvalues

#### Principal Component Analysis Tutorial

Recently I have been reading up on **Principal Component Analysis (PCA)**. PCA is essentially a compression technique, providing a means of representing larger or high dimensional data sets with less information. 1,592 more words

#### Solution to the Clebsch puzzle

Here is the solution to the puzzle about the Clebsch graph I posed at the weekend. Since Gordon and Tony (and probably others) have already solved it, I am giving you my solution now. 459 more words

#### Principal Component Analysis Explained Visually

http://setosa.io/ev/principal-component-analysis/

“Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It’s often used to make data easy to explore and visualize.”

#### Linear Algebra II: Eigenvectors and Diagonalisability

This post continues the discussion of the Oxford first-year course Linear Algebra II. We’ve moved on from determinants, and are now considering eigenvalues and eigenvectors of matrices and linear maps. 1,436 more words

#### Symmetric Matrices and Positive Definiteness

Symmetric matrices have three important properties that make it the ‘ideal’ matrix to have:

1.) Symmetric matrices satisfy

2.) For a symmetric matrix with real entries, the eigenvalues are also real… 531 more words

#### Differential equations and exp(At)

How would you solve the following system of linear first order differential equations?

We can write this in matrix form:

Where and . Our first step is to write our system of differential equations in the form . 504 more words