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For visualisation of the feature distribution on the UMAP space
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Would be nice to have the following methods
- [x] PCA - [ml-pca](https://github.com/mljs/pca)
- [x] Singular Value Decomposition (SVD) - [ml-matrix](https://github.com/mljs/matrix/blob/master/src/dc…
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thread dedicated to the knn task
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2021/03/31 Claus Wilke
https://wilkelab.org/SDS375/slides/dimension-reduction-1.html#1
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hi,
as I see in dimension Reduction you add a dense layer , and then you used PCA component for dense layer.
so what is the benefit of using PCA component, and why you use it?
can we just add a den…
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Perform PCA on the signals to see if it can extrapolate a deterministic signal in important linearly independent eigenvectors used to describe the samples. #4
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If you choose the L landmark molecule, theoretically, you will get an L dimension. Now you reduce to be D dimension.
I am curious about (1) Why is dimension reduction necessary? Can you directly trai…
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Hey DCP participants, good to see you here
This issue will helps readers in giving all the guidance that one needs to learn about Dimensionality Reduction. Tutorial to Dimensionality Reduction and …
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Hi,
If you have a dataset with a lot of factors and not all of them are important, how can you filter the unimportant variables (Dimensionality reduction) to improve your prediction accuracy?
Regard…
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I know we talked about this paper on zoom last time and you have it in your reference list.
[Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., Gar…