Random Forest-Based Diffusion Information Geometry for Supervised Visualization and Data Exploration
Class
Article
College
College of Science
Department
Mathematics and Statistics Department
Faculty Mentor
Kevin Moon
Presentation Type
Poster Presentation
Abstract
Most dimensionality reduction techniques to date are unsupervised; they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap). Such methods require large amounts of data and are often sensitive to noise that may obfuscate important patterns in the data. Various attempts at supervised dimensionality reduction methods that take into account auxiliary annotations (e.g., class labels) have been successfully implemented with goals of increased classification accuracy or improved data visualization. In this paper, we describe a novel supervised visualization technique based on random forest proximities and a diffusion-based information geometry. We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structure in data, while demonstrating the spatial relevance of features important for the supervised task. Importantly, our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.
Location
Logan, UT
Start Date
4-11-2021 12:00 AM
Included in
Random Forest-Based Diffusion Information Geometry for Supervised Visualization and Data Exploration
Logan, UT
Most dimensionality reduction techniques to date are unsupervised; they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap). Such methods require large amounts of data and are often sensitive to noise that may obfuscate important patterns in the data. Various attempts at supervised dimensionality reduction methods that take into account auxiliary annotations (e.g., class labels) have been successfully implemented with goals of increased classification accuracy or improved data visualization. In this paper, we describe a novel supervised visualization technique based on random forest proximities and a diffusion-based information geometry. We show, both qualitatively and quantitatively, the advantages of our approach in retaining local and global structure in data, while demonstrating the spatial relevance of features important for the supervised task. Importantly, our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.