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]^{[non-primary source needed]} adding a manifold constraint to the general problem of correlating sets of high-dimensional vectors.^[2]

^ 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). Archived from the original (PDF) on 2022-03-09.
^ Hotelling, H (1936). "Relations between two sets of variates" (PDF). Biometrika. 28 (3–4): 321–377. doi:10.2307/2333955. JSTOR 2333955.

[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). Archived from the original (PDF) on 2022-03-09.

[2] Hotelling, H (1936). "Relations between two sets of variates" (PDF). Biometrika. 28 (3–4): 321–377. doi:10.2307/2333955. JSTOR 2333955.

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