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This issue is a follow up from our meeting on Thursday, August 1st.
We need to further discuss what projection/dimension reduction technique we implement. Mentioned during the meeting were Principa…
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This error occurs when I do dynamic execution work with `keras.layers.UnitNormalization`, but not when I do static inference.
The code below is executed in the tensorflow backend environment.
```pyt…
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**Is your feature request related to a problem? Please describe.**
I would like to have a GPU-accelerated version of the PaCMAP algorithm, a competitor to UMAP. Personally, I would use this with t…
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### Your idea
Hi @bids-maintenance & everyone,
I hope you're doing fine.
This issue is meant to track/gauge interest and progress for a specification focusing on dimensionality reduction-based …
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Hello Leland,
Thank you for sharing this new algorithm.
I have a question regarding evaluation measures of dimensionality reduction methods. I'm aware of trustworthiness and continuity, but I'm lo…
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Consider PCA'ing down to 90% of variance explained.
For the All_Features PCA, this MIGHT make it possible to run models on laptops (750 features still a lot though)
could also go down to 85%, th…
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For visualisation of the feature distribution on the UMAP space
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Implementation of the following:
- [x] Principal Component Analysis (PCA)
- [x] Principal Component Regression (PCR)
- [x] Partial Least Squares Regression (PLSR)
- [ ] Sammon Mapping
-…
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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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