  The anchor words algorithm performs provably efficient topic model inference
by finding an approximate convex hull in a high-dimensional word co-occurrence
space. However, the existing greedy algorithm often selects poor anchor words,
reducing topic quality and interpretability. Rather than finding an approximate
convex hull in a high-dimensional space, we propose to find an exact convex
hull in a visualizable 2- or 3-dimensional space. Such low-dimensional
embeddings both improve topics and clearly show users why the algorithm selects
certain words.
