<p>I'd say you can. "Coordinates of point of importance inside image" qualifies as a feature so you can use it in supervised learning.
If clustering is a way to discover that coordinate you can use that.</p>

<p>A practical example: say you want to use "x-coordinate of left eye" as a feature in a classification problem. It does not matter how you get the object's values for that feature, as long as the values are accurate. If you have an algorithm (e.g. a clustering algorithm) which can compute the values you can use it.</p>

CC BY-SA 3.0
2013-01-30T00:02:20.810Z
2013-01-30T00:02:20.810Z
dtech
<p>Suppose you have a data set composed of n images as training examples. You run clustering on each image ( initializing 3 clusters per image) and learn the centers. Is it ok to then take the cluster centers themselves as features for a supervised learning algorithm and thus have a vocabulary for each image that way or is it inconsistent ? Are there are other more consistent measures that can be used ? </p>

CC BY-SA 3.0
2013-01-29T15:35:55.967Z
Voting to close as "not a real question".  I assume an "image" is a pixelmap, but in this context, what's a "feature"?  What's a "center"?  What's a "vocabulary"?  What is "inconsistent"?  Why three clusters, and not 2 or 17?  **What problem are you actually trying to solve?**
Jeffε
CC BY-SA 3.0
2013-02-01T02:03:57.553Z
@JɛﬀE I think valentin are talking about the Bag of Words algorithm.
Shawn Xie
CC BY-SA 3.0
2013-02-01T17:13:54.823Z
Yes indeed I was having the Bag-of-Words Algorithm on my mind. "Mea culpa" for not being explicit enough.  Thanks.
valentin
<machine-learning>
K means feature learning
valentin
# K means feature learning

(Asked by: username_0 on 2013-01-29)

Suppose you have a data set composed of n images as training examples. You run clustering on each image ( initializing 3 clusters per image) and learn the centers. Is it ok to then take the cluster centers themselves as features for a supervised learning algorithm and thus have a vocabulary for each image that way or is it inconsistent ? Are there are other more consistent measures that can be used ?

**Tags:** machine-learning

> **Comments:**
> **username_1**: Voting to close as "not a real question".  I assume an "image" is a pixelmap, but in this context, what's a "feature"?  What's a "center"?  What's a "vocabulary"?  What is "inconsistent"?  Why three clusters, and not 2 or 17?  **What problem are you actually trying to solve?**  
> **username_2**: @JɛﬀE I think username_0 are talking about the Bag of Words algorithm.  
> **username_0**: Yes indeed I was having the Bag-of-Words Algorithm on my mind. "Mea culpa" for not being explicit enough.  Thanks.  

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## 1 answers

### Answer by username_3 on 2013-01-30. Score: -1 | Accepted answer

I'd say you can. "Coordinates of point of importance inside image" qualifies as a feature so you can use it in supervised learning.
If clustering is a way to discover that coordinate you can use that.

A practical example: say you want to use "x-coordinate of left eye" as a feature in a classification problem. It does not matter how you get the object's values for that feature, as long as the values are accurate. If you have an algorithm (e.g. a clustering algorithm) which can compute the values you can use it.


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