Body: <p>Below is a statement from Machine Learning book, by E. Alpyadin, 4th edition, page 131:</p>
  <p><a href="https://i.sstatic.net/N8Ii3.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/N8Ii3.png" alt="enter image description here" /></a></p>
  <p>Question: Why does in  feature embedding  the similarities between instances in the new space respects the original pairwise similarities?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-08T01:38:21.880Z
Comments.Text: What's $X$ here? Is it the original instances or is it the embedded instances?
Comments.UserDisplayName: Alexander Wan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-08T10:31:32.363Z
Comments.Text: Original instances
Comments.UserDisplayName: DSPinfinity
Tags: <machine-learning><dimensionality-reduction>
Title: Why does in feature embedding the similarities between instances in the new space respects the original pairwise similarities?
UserDisplayName: DSPinfinity
ThreadText: # Why does in feature embedding the similarities between instances in the new space respects the original pairwise similarities?
  
  (Asked by: username_0 on 2023-10-07)
  
  Below is a statement from Machine Learning book, by E. Alpyadin, 4th edition, page 131:
  
  [![enter image description here](https://i.sstatic.net/N8Ii3.png)](https://i.sstatic.net/N8Ii3.png)
  
  Question: Why does in feature embedding the similarities between instances in the new space respects the original pairwise similarities?
  
  **Tags:** machine-learning, dimensionality-reduction
  > **Comments:**
  > **username_1**: What's $X$ here? Is it the original instances or is it the embedded instances?  
  > **username_0**: Original instances  
  
  ---
  
  ## 0 answers
  
  