Picasso: Model-free Feature Visualization
This addresses the challenge of efficiently browsing and curating features for ML practitioners, though it appears incremental as it builds on existing visualization methods.
The paper tackles the problem of visualizing large feature sets in machine learning by presenting a novel approach that can display up to several thousand features in a single image, showing individual features and their interactions through relative positioning.
Today, Machine Learning (ML) applications can have access to tens of thousands of features. With such feature sets, efficiently browsing and curating subsets of most relevant features is a challenge. In this paper, we present a novel approach to visualize up to several thousands of features in a single image. The image not only shows information on individual features, but also expresses feature interactions via the relative positioning of features.