Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum

@article{Thiagarajan2019UnsupervisedDS,
  title={Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum},
  author={Jayaraman J. Thiagarajan and Rushil Anirudh and Rahul Sridhar and Peer-Timo Bremer},
  journal={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2019},
  pages={5436-5440}
}
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 spaces. Consequently, dimension selection (DS) aims… Expand

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