Function Preserving Projection (FPP): A Linear Projection Technique for Visual Interpretation of High-Dimensional Functions
Shusen Liu, USDOE National Nuclear Security Administration (NNSA), Rushil Anirudh, Jayaraman J. Thiagarajan, Peer‐Timo Bremer · 2019
In this work, we propose function preserving projection (FPP), a new class of scalable linear projection techniques for finding interpretable structure in highdimensional datasets. Instead of focusing on preserving the structure of highdimensional points as traditional dimensionality reduction methods, we aim to find a 2D view of the data, in which human interpretable patterns of meaningful properties (that defined on the high-dimensional points, i.e., functions in a highdimensional domain) are revealed. Such a visual-centric approach introduces new avenues for exploratory data analysis and allows efficient implementation that provides a scalable tool for exploring potentially unknown or unexpected relationships in complex and large high-dimensional data (e.g., beyond millions of samples and tens of thousands of dimensions).