  Embedding is a common technique for analyzing multi-dimensional data.
However, the embedding projection cannot always form significant and
interpretable visual structures that foreshadow underlying data patterns. We
propose an approach that incorporates human knowledge into data embeddings to
improve pattern significance and interpretability. The core idea is (1)
externalizing tacit human knowledge as explicit sample labels and (2) adding a
classification loss in the embedding network to encode samples' classes. The
approach pulls samples of the same class with similar data features closer in
the projection, leading to more compact (significant) and class-consistent
(interpretable) visual structures. We give an embedding network with a
customized classification loss to implement the idea and integrate the network
into a visualization system to form a workflow that supports flexible class
creation and pattern exploration. Patterns found on open datasets in case
studies, subjects' performance in a user study, and quantitative experiment
results illustrate the general usability and effectiveness of the approach.
