A General Framework for Visualizing Embedding Spaces of Neural Survival
  Analysis Models Based on Angular Information
George H. Chen
We propose a general framework for visualizing any intermediate embedding
representation used by any neural survival analysis model. Our framework is
based on so-called anchor directions in an embedding space. We show how to
estimate these anchor directions using clustering or, alternatively, using
user-supplied "concepts" defined by collections of raw inputs (e.g., feature
vectors all from female patients could encode the concept "female"). For
tabular data, we present visualization strategies that reveal how anchor
directions relate to raw clinical features and to survival time distributions.
We then show how these visualization ideas extend to handling raw inputs that
are images. Our framework is built on looking at angles between vectors in an
embedding space, where there could be "information loss" by ignoring magnitude
information. We show how this loss results in a "clumping" artifact that
appears in our visualizations, and how to reduce this information loss in
practice.