davisidarta/fastlapmap
Fast Laplacian Eigenmaps: lightweight multicore LE for non-linear dimensional reduction with minimal memory usage. Outperforms sklearn's implementation and escalates linearly beyond 10e6 samples.
This tool helps data scientists and machine learning engineers analyze very large datasets by simplifying complex, high-dimensional data into a lower-dimensional representation. It takes your raw data, such as images or text, and outputs a reduced version that is easier to visualize or feed into other machine learning models. This is especially useful for tasks like clustering or classification, where understanding underlying patterns in big data is crucial.
No commits in the last 6 months. Available on PyPI.
Use this if you need to perform non-linear dimensionality reduction on large datasets (millions of samples) and require significantly faster processing than standard methods, with minimal memory use.
Not ideal if your datasets are small, or if you require a simple linear dimensionality reduction technique like PCA.
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23
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1
Language
Python
License
MIT
Category
Last pushed
Nov 12, 2021
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