A Subspace Projection Based Technique for Visualizing Machine Learning Models

Ziqian Bi, Raymond Gao, Shiaofen Fang

2025

Abstract

As Artificial Intelligence (AI) technology, particularly Machine Learning (ML) algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and ML algorithms become increasingly desirable. Visualization is a common tool to help users understand individual ML decision-making processes, but its use in demonstrating the global patterns and trends of a ML model has not been sufficiently explored. In this paper, we present a visualization technique using subspace projection to visualize ML models as scalar valued multi-dimensional functions to help users understand the global behaviors of the models in different 2D viewing spaces. A formal definition of the visualization problem will be given. The visualization technique is developed using an interpolation-based subspace morphing algorithm and a subspace sampling method to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. Compared to existing ML visualization methods, our work provides better global views and allows the users to select viewing spaces to provide user-specified perspectives. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks.

Paper Citation

in Harvard Style

Bi Z., Gao R. and Fang S. (2025). A Subspace Projection Based Technique for Visualizing Machine Learning Models. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP; ISBN 978-989-758-728-3, SciTePress, pages 887-894. DOI: 10.5220/0013132100003912

in Bibtex Style

@conference{ivapp25,
author={Ziqian Bi and Raymond Gao and Shiaofen Fang},
title={A Subspace Projection Based Technique for Visualizing Machine Learning Models},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP},
year={2025},
pages={887-894},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013132100003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP
TI - A Subspace Projection Based Technique for Visualizing Machine Learning Models
SN - 978-989-758-728-3
AU - Bi Z.
AU - Gao R.
AU - Fang S.
PY - 2025
SP - 887
EP - 894
DO - 10.5220/0013132100003912
PB - SciTePress