<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>

CC BY-SA 4.0
2023-10-08T01:38:21.880Z
What's $X$ here? Is it the original instances or is it the embedded instances?
Alexander Wan
CC BY-SA 4.0
2023-10-08T10:31:32.363Z
Original instances
DSPinfinity
<machine-learning><dimensionality-reduction>
Why does in feature embedding the similarities between instances in the new space respects the original pairwise similarities?
DSPinfinity
# 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  

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## 0 answers

