Skip to Main content Skip to Navigation
Conference papers

On the Spectrum of Random Features Maps of High Dimensional Data

Abstract : Random feature maps are ubiquitous in modern statistical machine learning, where they generalize random projections by means of powerful, yet often difficult to analyze nonlinear operators. In this paper, we leverage the "concentration" phenomenon induced by random matrix theory to perform a spectral analysis on the Gram matrix of these random feature maps, here for Gaussian mixture models of simultaneously large dimension and size. Our results are instrumental to a deeper understanding on the interplay of the nonlinearity and the statistics of the data, thereby allowing for a better tuning of random feature-based techniques.
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01954933
Contributor : Zhenyu Liao <>
Submitted on : Tuesday, May 19, 2020 - 3:54:31 PM
Last modification on : Tuesday, December 8, 2020 - 10:31:12 AM

File

couillet_RFM_ICML18.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01954933, version 1
  • ARXIV : 1805.11916

Citation

Zhenyu Liao, Romain Couillet. On the Spectrum of Random Features Maps of High Dimensional Data. International Conference on Machine Learning (ICML 2018), Jul 2018, Stockholm, Sweden. ⟨hal-01954933⟩

Share

Metrics

Record views

196

Files downloads

15