Skip to Main content Skip to Navigation
Conference papers

Random matrix-improved kernels for large dimensional spectral clustering

Abstract : Leveraging on recent random matrix advances in the performance analysis of kernel methods for classification and clustering, this article proposes a new family of kernel functions theoretically largely outperforming standard kernels in the context of asymp-totically large and numerous datasets. These kernels are designed to discriminate statistical means and covariances across data classes at a theoretically minimal rate (with respect to data size). Applied to spectral clustering, we demonstrate the validity of our theoretical findings both on synthetic and real-world datasets (here, the popular MNIST database as well as EEG recordings on epileptic patients). Index Terms— Spectral clustering, inner product kernels, random matrix theory.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download
Contributor : Hafiz Tiomoko Ali <>
Submitted on : Monday, June 11, 2018 - 10:25:42 AM
Last modification on : Wednesday, April 8, 2020 - 3:29:15 PM
Document(s) archivé(s) le : Thursday, September 13, 2018 - 12:18:51 AM


Files produced by the author(s)



Hafiz Tiomoko Ali, Abla Kammoun, Romain Couillet. Random matrix-improved kernels for large dimensional spectral clustering. 2018 IEEE Statistical Signal Processing Workshop (SSP), Jun 2018, Freiburg, Germany. ⟨10.1109/ssp.2018.8450705⟩. ⟨hal-01812009⟩



Record views


Files downloads