https://github.com/reichmla/lsp-python

lsp-python is a lightweight implementation of the least square projection (LSP) dimensionality reduction technique using a sklearn style API.
https://github.com/reichmla/lsp-python

clustering dimensionality-reduction dr least-squares-projection lsp mds multidimensional-scaling projection sklearn

Last synced: 9 days ago
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lsp-python is a lightweight implementation of the least square projection (LSP) dimensionality reduction technique using a sklearn style API.

• Host: GitHub
• URL: https://github.com/reichmla/lsp-python
• Owner: reichmla
• License: mit
• Created: 2024-05-27T20:14:58.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-30T11:42:31.000Z (over 1 year ago)
• Last Synced: 2025-06-15T16:19:12.197Z (7 months ago)
• Topics: clustering, dimensionality-reduction, dr, least-squares-projection, lsp, mds, multidimensional-scaling, projection, sklearn
• Language: Python
• Homepage:
• Size: 258 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# lsp-python

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](tests/iris_example.py) and [tests/digits_example.py](tests/digits_example.py).

## Installation

The library currently only supports Python 3.11.

### Dependencies

The library depends on the following packages:

- numpy

- scikit-learn

- matplotlib

### Pip

The library can be installed using pip:

```bash

pip install lsp-python

```