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### Features Reduction
It would be great if dimensionality reduction API could be added to ML.NET. This will be a major advantage in shortening training time.
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Right now there is a distinction between `feature-extraction` and `feature-extraction-model` (curious on the distinction, I think I get it: one is the general method, the other one is a model that re…
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(nllp) MacBook-Air-3:lnlp user$ python main.py tests/test_data/data.csv
Welcome to the NLLP CLI!
Loaded data from tests/test_data/data.csv
1. Run a Topic Model
2. Run an Optimization routine fo…
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Original [issue 104](https://code.google.com/p/cleartk/issues/detail?id=104) created by ClearTK on 2009-08-05T15:37:07.000Z:
The following is from a posting by Olivier Grisel. This is something we
s…
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product quantization would be a good start
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#### Description
Hello! Is there a way to inverse_transform any of the 'approximation' methods? I have a dataset of simple 20,0001D timeseries only 30 units long (i.e., [20,000 x 30]) and I want to r…
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In the long run, depending on the number of metrics, we should consider a visual 2D dimensionality reduction (t-SNE?) of the selected metrics to identify possible correlations. However, before we do t…
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**Is your feature request related to a problem? Please describe.**
The `TICA` and `VAMP` decomposition classes both provide similar interfaces for `.fit_from_timeseries(data)`. However, the `TICA` cl…