Q:

Is it feasible to use t-SNE to reduce a dataset to one dimension?

Is it feasible to use t-SNE to reduce a dataset to one dimension?
Suppose that I have a matrix, $X$, can I reduce it to a column vector, $Y$ with t-SNE?
Suppose that $X$ has 100 columns, how much information can I expect to lose by reducing it to just a column vector with t-SNE (if possible)?
References:
L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. 
• L.J.P. van der Maaten. Learning a Parametric Embedding by Preserving Local Structure. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AI-STATS), JMLR W&CP 5:384-391, 2009. 
• L.J.P. van der Maaten. Barnes-Hut-SNE. In Proceedings of the International Conference on Learning Representations, 2013. 
All text available at: http://homepage.tudelft.nl/19j49/t-SNE.html

A:

Yes, why not. t-SNE can be used to reduce any dimension to one-dimension. The question about how much information will get lost is not so simple. It is even not clear how to measure or define the information of a high dimensional data.

