Suggested further readings

Principal component analysis (PCA):

Shlens, J. (2014). A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100.

Other linear dimensionality reductions:

Hyvarinen, A., and Oja, E. (2000). Independent component analysis: algorithms and applications. Neural networks 13(4-5): 411-430. doi: 10.1016/S0893-6080(00)00026-5 (preprint: www.cse.msu.edu/~cse902/S03/icasurvey.pdf ).

Gillis, N. (2014) The why and how of nonnegative matrix factorization. arXiv preprint arxiv:1401.5226.

Nonlinear dimensionality reduction:

Coenen, A., and Pearce, A. Understanding UMAP. url: pair-code.github.io/understanding-umap/.

Wattenberg, M., Viegas, F., and Johnson, I. (2016). How to Use t-SNE Effectively. Distill. doi: 10.23915/distill.00002 .

Dimensionality reduction in systems neuroscience:

Cunningham, J. P., and Byron, M. Y. (2014). Dimensionality reduction for large-scale neural recordings. Nature neuroscience 17(11): 1500-1509. doi: 10.1038/nn.3776 (postprint: europepmc.org/articles/pmc4433019?pdf=render ).

Brain-computer interface work shown in Outro:

Golub, M. D., Chase, S. M., Batista, A. P., and Byron, M. Y. (2016). Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control. Current opinion in neurobiology 37: 53-58. doi: 10.1016/j.conb.2015.12.005 .

Golub, M. D., Sadtler, P. T., Oby, E. R., Quick, K. M., Ryu, S. I., Tyler-Kabara, E. C., … and Yu, B. M. (2018). Learning by neural reassociation. Nature neuroscience 21(4): 607-616. doi: 10.1038/s41593-018-0095-3 (postprint: europepmc.org/articles/pmc5876156?pdf=render ).

Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., … and Batista, A. P. (2014). Neural constraints on learning. Nature 512(7515): 423-426. doi: 10.1038/nature13665 (postprint: europepmc.org/articles/pmc4393644?pdf=render ).