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### Description
Implementing an interface for dimensionality reduction
### Tasks
- [x] PCA
- [x] tSNA
- [x] UMAP
### Freeform Notes
_No response_
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For visualisation of the feature distribution on the UMAP space
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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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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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1. 일시 : 2024년 05월 31일(금) 오후 4시~5시
2. 장소 : 아산이학관 526호(권택연 세미나실)
3. 연사 : 정윤모 교수님 (성균관대학교)
4. 제목 : Formation-Controlled Dimensionality Reduction
5. 초록 : Dimensionality reduction represents th…
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Hi,
I would to begin by thanking you for your tremendous work on this library.
I had a question regarding your [dimensionality_reduction.py](https://github.com/UKPLab/sentence-transformers/blob/m…
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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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thread dedicated to the knn task
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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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