Intuitive Laplacien Eigenmap Visualization

Dimensionality reduction technique is a crucial technique in machine learning as they are an approach in finding the "principals" in high dimensional data, which tends to be very noisy and would affect the accuracy of simple classifier significantly. However, these techniques are usually very mathematically intense and complicated for the general public to understand without prior mathematical background. Therefore, we developed this visualization project using D3, JavaScript to deliver an intuitive understanding of one of the most popular dimensionality reduction algorithms (Laplacien Eigenmap) on the art work collections in MET, New York.

Principal components of the image space of artwork collections in MET.