Manifold alignment

Manifold alignment is a class of machine learning algorithms that produce projections between sets of data, given that the original data sets lie on a common manifold. The concept was first introduced as such by Ham, Lee, and Saul in 2003,[1] adding a manifold constraint to the general problem of correlating sets of high-dimensional vectors.[2]

  1. ^ Ham, Ji Hun; Daniel D. Lee; Lawrence K. Saul (2003). "Learning high dimensional correspondences from low dimensional manifolds" (PDF). Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003).
  2. ^ Hotelling, H (1936). "Relations between two sets of variates" (PDF). Biometrika. 28 (3–4): 321–377. doi:10.2307/2333955. JSTOR 2333955.