Discriminant Diffusion Maps Analysis

The demo shows how the discriminant diffusion maps analysis (DDMA) projects data from high-dimensional space to a low-dimensional space by generating a three-arm spiral data set of 3D, and then reducing the original data space to a 2-dimensional space.
The codes are shared here for expediating the communication of research results among scientific communities. They can be freely used at your own risk, given that the authors' contributions are appropriatedly cited or acknowledged in your publications.
References:
[1] Yixiang Huang, et al. Discriminant Diffusion Maps Analysis: A Robust Manifold Learner For Dimensionality Reduction And Its Applications in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 34(1-2), 2013: 277-297.