<p>I am looking to create a machine learning model that classifies types of light curves. But what features do I use? In other words what numbers or parameters fully describe a light curve and differentiate it from others? I understand that period and amplitude are obvious, but is there anything else that gives more info on the shape of the light curve?</p>

CC BY-SA 4.0
2024-10-30T07:44:22.713Z
You don't specify whether this is supervised or unsupervised learning snd what you mean by "classifies types". In some aporoaches (e.g. Random Forest) you could try every feature you can think of and see which is most powerful. In others (convolutional neural networks or SVD machines) you might use the data itsrlf snd go through a feature identification phase. I think your problen is underspecified.
ProfRob
CC BY-SA 4.0
2024-10-30T13:38:08.197Z
@ProfRob I think my real question is what parameters fully describe and allow us to differentiate light curves from each other, I'm not sure if this clears the question up though
Astrovis
CC BY-SA 4.0
2024-10-30T13:45:22.477Z
Well you can use ML to answer that question. Or a more "classical" PCA or eigenfunction analysis. The features may not be related to anything physically meaningful. I think you need to have a clearer aim than "differentiate light curves from each other".
ProfRob
CC BY-SA 4.0
2024-10-30T14:07:09.680Z
@ProfRob do you mean the features don't have to be anything meaningful for us, the ML model can just generate them (this might be more of a stack overflow question)
Astrovis
CC BY-SA 4.0
2024-10-30T17:32:59.583Z
Yes. So if you take the example of facial recognition - one approach is to construct "eigenfaces" by looking at the eigenfunctions of a matrix constructed by turning the image pixels into a raster along each column, then finding the eigenvectors ("eigenfaces") of the corresponding covariance matrix. Any face can then be reconstructed as a linear combination of say the 10 eigenfaces with the largest eigenvalues. The individual eigenfaces don't look much like faces at all. https://en.wikipedia.org/wiki/Eigenface
ProfRob
CC BY-SA 4.0
2024-10-30T17:36:26.087Z
You can then use another ML algorithm to define your classifications using the multiplicative factors required for each basis eigenvector as the features. I think this is quite a well-respected path in ML - use something like PCA to do "dimensional reduction" (i.e. turning your light curve into a smaller number of features) and then something like random forest to do the classification.
ProfRob
CC BY-SA 4.0
2024-10-31T05:16:44.763Z
@ProfRob Thanks, I'd accept this as an answer.
Astrovis
<python><light-curve>
What elements (features) of a light curve do I need to classify it?
Astrovis
# What elements (features) of a light curve do I need to classify it?

(Asked by: username_0 on 2024-10-30)

I am looking to create a machine learning model that classifies types of light curves. But what features do I use? In other words what numbers or parameters fully describe a light curve and differentiate it from others? I understand that period and amplitude are obvious, but is there anything else that gives more info on the shape of the light curve?

**Tags:** python, light-curve

> **Comments:**
> **username_1**: You don't specify whether this is supervised or unsupervised learning snd what you mean by "classifies types". In some aporoaches (e.g. Random Forest) you could try every feature you can think of and see which is most powerful. In others (convolutional neural networks or SVD machines) you might use the data itsrlf snd go through a feature identification phase. I think your problen is underspecified.  
> **username_0**: @username_1 I think my real question is what parameters fully describe and allow us to differentiate light curves from each other, I'm not sure if this clears the question up though  
> **username_1**: Well you can use ML to answer that question. Or a more "classical" PCA or eigenfunction analysis. The features may not be related to anything physically meaningful. I think you need to have a clearer aim than "differentiate light curves from each other".  
> **username_0**: @username_1 do you mean the features don't have to be anything meaningful for us, the ML model can just generate them (this might be more of a stack overflow question)  
> **username_1**: Yes. So if you take the example of facial recognition - one approach is to construct "eigenfaces" by looking at the eigenfunctions of a matrix constructed by turning the image pixels into a raster along each column, then finding the eigenvectors ("eigenfaces") of the corresponding covariance matrix. Any face can then be reconstructed as a linear combination of say the 10 eigenfaces with the largest eigenvalues. The individual eigenfaces don't look much like faces at all. https://en.wikipedia.org/wiki/Eigenface  
> **username_1**: You can then use another ML algorithm to define your classifications using the multiplicative factors required for each basis eigenvector as the features. I think this is quite a well-respected path in ML - use something like PCA to do "dimensional reduction" (i.e. turning your light curve into a smaller number of features) and then something like random forest to do the classification.  
> **username_0**: @username_1 Thanks, I'd accept this as an answer.  

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## 0 answers

