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 strongly
distorted or hidden by certain influential data attributes. This paper presents a feature
learning framework, FEALM, designed to generate an optimized set of data projections for
nonlinear DR in order to capture important patterns in the hidden manifolds. These …

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 …