Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden Patterns. (arXiv:2206.13891v1 [cs.LG])

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 …

arxiv dimensionality extraction feature learning lg patterns