ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping

Héléna Alexandra Gaspar

doi:10.5334/jors.235

ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping

Héléna Alexandra Gaspar^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

ugtm is a Python package that implements generative topographic mapping (GTM), a dimensionality reduction algorithm by Bishop, Svensén and Williams. Because of its probabilistic framework, GTM can also be used to build classification and regression models, and is an attractive alternative to t-distributed neighbour embedding (t-SNE) or other non-linear dimensionality reduction methods. The package is compatible with scikit-learn, and includes a GTM transformer (eGTM), a GTM classifier (eGTC) and a GTM regressor (eGTR). The input and output of these functions are numpy arrays. The package implements supplementary functions for GTM visualization and kernel GTM (kGTM). The code is under MIT license and available on GitHub (https://github.com/hagax8/ugtm). For installation instructions and documentation, cf. https://ugtm.readthedocs.io.

Original language	English
Pages (from-to)	1-5
Number of pages	5
Journal	Journal of Open Research Software
Volume	6
DOIs	https://doi.org/10.5334/jors.235
Publication status	Published - 2018

Keywords

classification
data analysis
data visualization
dimensionality reduction
generative topographic mapping
machine learning
regression

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10.5334/jors.235

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KW - regression

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ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping

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Keywords

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