<p>All the methods are basically manifold methods which are used to squeeze hyper dimensions to two or three dimensions with a certain amount of information loss. So whatever you "see" with these methods are not real and deceiving. You may see a separation of data points in 3D but when they are mapped onto the actual dimensions it may be complete rubbish. </p>

<p>By definition, these methods do not provide a way to find separating hyperplane. They just tell you some condensed information about the whole data. It is generally advised to use methods like t-SNE with caution. </p>

CC BY-SA 4.0
2019-05-04T19:02:15.120Z
2019-05-04T19:02:15.120Z
Arun Aniyan
<p>Is it possible to use t-SNE, PCA or UMAP to find separating hyperplane?</p>

<p>Assume we have data points in high dimensional space and we want to phase separate it into two sets of points? </p>

<p>Is there a way to use t-SNE, PCA or UMAP for such a task?</p>

<machine-learning><deep-learning>
Using UMAP, PCA or t-SNE to find the separating hyperplane?
0x90
# Using UMAP, PCA or t-SNE to find the separating hyperplane?

(Asked by: username_0 on 2019-05-02)

Is it possible to use t-SNE, PCA or UMAP to find separating hyperplane?

Assume we have data points in high dimensional space and we want to phase separate it into two sets of points?

Is there a way to use t-SNE, PCA or UMAP for such a task?

**Tags:** machine-learning, deep-learning


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## 1 answers

### Answer by username_1 on 2019-05-04. Score: 3

All the methods are basically manifold methods which are used to squeeze hyper dimensions to two or three dimensions with a certain amount of information loss. So whatever you "see" with these methods are not real and deceiving. You may see a separation of data points in 3D but when they are mapped onto the actual dimensions it may be complete rubbish.

By definition, these methods do not provide a way to find separating hyperplane. They just tell you some condensed information about the whole data. It is generally advised to use methods like t-SNE with caution.


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