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control points             |  64 control points | 256 control points             |  512 control points\n:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:\n![](./assets/digits_32.png)  |  ![](./assets/digits_64.png)  |  ![](./assets/digits_256.png)  |  ![](./assets/digits_512.png)\n\n# lsp-python\n\nlsp-python is a lightweight python implementation of the Least Square Projection (LSP) dimensionality reduction technique using sklearn style API.\n\nThe 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:\n\n```\n@ARTICLE{4378370,\n  author={Paulovich, Fernando V. and Nonato, Luis G. and Minghim, Rosane and Levkowitz, Haim},\n  journal={IEEE Transactions on Visualization and Computer Graphics}, \n  title={Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping}, \n  year={2008},\n  volume={14},\n  number={3},\n  pages={564-575},\n  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},\n  doi={10.1109/TVCG.2007.70443}}\n```\n\nA small working example can be found in [tests/iris_example.py](tests/iris_example.py) and [tests/digits_example.py](tests/digits_example.py).\n\n## Installation\nThe library currently only supports Python 3.11.\n\n### Dependencies\nThe library depends on the following packages:\n- numpy\n- scikit-learn\n- matplotlib\n\n### Pip\nThe library can be installed using pip:\n\n```bash\npip install 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