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Generate multiple image from multivariate time series #95
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Thank you @johannfaouzi, that's super helpful! I'm interested in the cosine similarity you mentioned - some context: I'm working on training CNNs with EEG data for BCI systems for my undergrad thesis. Multivariate Time Series Data Transformation for Convolutional Neural Network describes a simple way of getting 1 single image by simply appending the variables images(in my case possibly channels/trials images). So, what you're suggesting is another option to "join the data", but instead of appending images, we generate a single matrix having the angles, i.e. instead of having N trials/channels images we'd have a matrix with the angle between the trials/channels as a single scalar and generate 1 single image from that matrix "having all the information"? |
Indeed I think that there are two main approaches:
You will find attached a notebook in which I tried several approaches based on cosine similarity (or linear kernel). Here is also a Google Colab link. Hope this helps you a bit. |
@johannfaouzi |
@hvsw |
Description
I was wondering, is
fitted.shape
equal to (6, 6, 6) because it fitted and transformed for each feature and generated an image for each one, so I have 6 6x6 images?Steps/Code to Reproduce
This code shows what I mean.
fitted.shape
is(6, 6, 6)
.Versions
NumPy 1.20.2
SciPy 1.6.2
Scikit-Learn 0.24.1
Numba 0.53.1
Pyts 0.11.0
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