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Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden Patterns. (arXiv:2206.13891v1 [cs.LG])
June 29, 2022, 1:10 a.m. | Takanori Fujiwara, Yun-Hsin Kuo, Anders Ynnerman, Kwan-Liu Ma
cs.LG updates on arXiv.org arxiv.org
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
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