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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

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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.

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  • $\begingroup$ Thanks. I intend to use this for clustering. Given enough domain knowledge, I should be able to sense-check the clusters. $\endgroup$
    – power
    Nov 17, 2013 at 22:50
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    $\begingroup$ I have recently tested tSNE, CCA and RPM algorithms to reduce 1500 dimensional data to one dimension. tSNE worked pretty well, but not the best from a particular point of view (see jamesxli.blogspot.ca/2013/11/on-multidimensional-sorting.html). To make a judgement about the performance, I would trust more domain knowledge or visual checking, than ad-hoc statistics. $\endgroup$
    – James LI
    Nov 17, 2013 at 23:49

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