Skip to Main content Skip to Navigation
Conference papers

Kernel random matrices of large concentrated data: the example of GAN-generated images

Abstract : Based on recent random matrix advances in the analysis of kernel methods for classification and clustering, this paper proposes the study of large kernel methods for a wide class of random inputs, i.e., concentrated data, which are more generic than Gaussian mixtures. The concentration assumption is motivated by the fact that one can use generative models to design complex data structures, through Lipschitz-ally transformed concentrated vectors (e.g., Gaussian) which remain concentrated vectors. Applied to spectral clustering, we demonstrate that our theoretical findings closely match the behavior of large kernel matrices, when considering the fed-in data as CNN representations of GAN-generated images (i.e., concentrated vectors by design).
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download
Contributor : Mohamed El Amine Seddik Connect in order to contact the contributor
Submitted on : Monday, October 19, 2020 - 12:19:31 PM
Last modification on : Tuesday, October 19, 2021 - 11:25:56 AM
Long-term archiving on: : Wednesday, January 20, 2021 - 6:37:27 PM


Files produced by the author(s)



Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet. Kernel random matrices of large concentrated data: the example of GAN-generated images. ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. ⟨10.1109/ICASSP.2019.8683333⟩. ⟨hal-02971224⟩



Record views


Files downloads