  Dimensionality reduction (DR) plays a vital role in the visual analysis of
high-dimensional data. One main aim of DR is to reveal hidden patterns that lie
on intrinsic low-dimensional manifolds. However, DR often overlooks important
patterns when the manifolds are distorted or masked by certain influential data
attributes. This paper presents a feature learning framework, FEALM, designed
to generate a set of optimized data projections for nonlinear DR in order to
capture important patterns in the hidden manifolds. These projections produce
maximally different nearest-neighbor graphs so that resultant DR outcomes are
significantly different. To achieve such a capability, we design an
optimization algorithm as well as introduce a new graph dissimilarity measure,
named neighbor-shape dissimilarity. Additionally, we develop interactive
visualizations to assist comparison of obtained DR results and interpretation
of each DR result. We demonstrate FEALM's effectiveness through experiments and
case studies using synthetic and real-world datasets.
