Self-organising map on the Iris dataset
A self-organising map is a dimensionality reduction technique -- given high-dimensional data, it will generate a 'map' which separates that data spatially in a lower number of dimensions.
This video visualises my implementation of a 2D self-organising map working on the Iris data set. This data set has four dimensions and three categories. In the video, at each timestep, the colour of each element shows the category which it responds to best (as determined by the sum of Euclidean distances between its weight vectors and each input, per category).