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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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**Describe the problem concisely**.
Include references to papers where the task is attempted.
This issue introduces the subtask of supervised dimensionality reduction - some dimensionality reduction…
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The diffusion map is a dimensionality reduction technique that uses a transitional probability as its "distance" measure. It is noise-resistant and non-linear. Moreover, the algorithm itself is fast a…
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This should support a variety of dimensionality reduction techniques, as well as allowing the embeddings from inference time to be reused.
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nomic-embed-text-v1.5 is an improvement upon [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1) that utilizes [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) whi…
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**Is your feature request related to a problem? Please describe.**
The package currently only applies UMAP dimensionality reduction. More dimensionality reduction techniques, like t-SNE and PCA, co…
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We already have the building blocks for ANN. @krstopro, how much work do you believe it is to also have it for manifold/dimensionality reduction?
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- [ ] Enable selection of a machine where a Python-script is executed.
- [ ] Validate the performance gain from the RAPIDS use compared to the native dimensionality reduction implementation.
- [ ] A…
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In addition to, or in replacement of, PCAs, we might experiment with t-SNE: https://github.com/cemoody/topicsne
rvosa updated
7 years ago