Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum
ICASSP 2019
Pages: 5436 - 5440
Published: May 12, 2019
Abstract
Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed...
Paper Details
Title
Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum
DOI
Published Date
May 12, 2019
Journal
Pages
5436 - 5440
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