Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-04T19:02:15.120Z
Answers.LastActivityDate: 2019-05-04T19:02:15.120Z
Answers.UserDisplayName: Arun Aniyan
Body: <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>
  
Tags: <machine-learning><deep-learning>
Title: Using UMAP, PCA or t-SNE to find the separating hyperplane?
UserDisplayName: 0x90
ThreadText: # 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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