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lsp-python is a lightweight python implementation of the Least Square Projection (LSP) dimensionality reduction technique using sklearn style API.
The implementation is based on the paper "Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping", which can be cited using:
@ARTICLE{4378370, author={Paulovich, Fernando V. and Nonato, Luis G. and Minghim, Rosane and Levkowitz, Haim}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping}, year={2008}, volume={14}, number={3}, pages={564-575}, keywords={Least squares methods;Multidimensional systems;Data visualization;Least squares approximation;Data analysis;Computational geometry;Testing;Text processing;Data mining;Demography;Multivariate visualization;Data and knowledge visualization;Information visualization;Multivariate visualization;Data and knowledge visualization;Information visualization}, doi={10.1109/TVCG.2007.70443}} A small working example can be found in tests/iris_example.py and tests/digits_example.py.
The library currently only supports Python 3.11.
The library depends on the following packages:
- numpy
- scikit-learn
- matplotlib
The library can be installed using pip:
pip install lsp-python