I decided not to spend much time on it and allow you to read this article within a reasonable time. not providing a built-in way to map new data points to the corresponding. More of how this distribution was selected and why Gaussian is not the best idea you can find in the paper. Classification accuracy for PCA, Sammons mapping, and t-SNE was, respectively. To solve that we’re going to use Student t-distribution with a single degree of freedom. One of the properties of Gaussian is that it has a “short tail” and because of that it creates a crowding problem. The most obvious choice for new distribution would be to use Gaussian again. The goal of this algorithm is to find similar probability distribution in low-dimensional space. Points should be spread randomly on a new space. The next part of t-SNE is to create low-dimensional space with the same number of points as in the original space. That helps distinguish neighbor’s probabilities and because you’ve already understood the whole process you should be able to adjust it to new values. I’m far better with explaining things visually so this is going to be our dataset: How t-SNE works? Probability Distribution That makes it extremely useful when dealing with CNN networks. t-SNE is mostly used to understand high-dimensional data and project it into low-dimensional space (like 2D or 3D). That’s helpful when you need to try to reduce your feature list and reuse matrix created from train data.
You can get that matrix and apply it to a new set of data with the same result. PCA uses the global covariance matrix to reduce data. You need to remember that t-SNE is iterative so unlike PCA you cannot apply it on another dataset. That’s why it’s important to know at least one algorithm that deals with linearly nonseparable data.
If you want to play with those examples go and visit Distill.Īs you can imagine, those examples won’t return any reasonable results when parsed through PCA (ignoring the fact that you’re parsing 2D into 2D). Linearly nonseparable data, Source: CC-BY 2.0
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