t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
So it sounds pretty great, but that is the Author talking.
What have you taken away from this competition?
Always visualize your data first, before you start to train predictors on the data! Oftentimes, visualizations such as the ones I made provide insight into the data distribution that may help you in determining what types of prediction models to try.
Information must1 be being lost -- it is a dimensionality reduction technique afterall. However, as it is a good technique to use when visualising, the information lost is less valuable than the information highlighted (/made visible/comprehend-able through reduction to 2 or 3 dimensions).
So my question is:
- When is tSNE the wrong tool for the job?
- What kind of datasets cause it to not function,
- What kind of questions does it look like it can answer, but it actually can not?
- In the second quote above it is recommended to always visualise your dataset, should this visualisation always be done with tSNE?
I expect that this question might be best answered in the converse, ie answering: When is tSNE the right tool for the job?
I have been cautioned not to rely on tSNE to tell me how easy data will be classifiable (separated into classes -- a discriminative model) The example of it being misleading was, that, for the two images below, a generative model2 was worse for the data visualised in the first/left (accuracy 53.6%) than an equivalent one for the second/right (accuracy 67.2%).
1 I could be wrong about this I may sit down and try at a proof/counter example later
2 note that a generative model is not the same as a discriminitive model, but this is the example I was given.