JMLRJan 9, 2025

'Localisation of Regularised and Multiview Support Vector Machine Learning', by Aurelian Gheondea, Cankat Tilki.

http://jmlr.org/papers/v25/23-0522.html

#lossfunctions #kernels #semidefinite

Localisation of Regularised and Multiview Support Vector Machine Learning

JMLRJan 5, 2025

'Distributed Kernel-Driven Data Clustering', by Ioannis Schizas.

http://jmlr.org/papers/v25/23-0669.html

#clustering #distributed #semidefinite

Distributed Kernel-Driven Data Clustering

JMLRAug 4, 2024

'Efficient Convex Algorithms for Universal Kernel Learning', by Aleksandr Talitckii, Brendon Colbert, Matthew M. Peet.

http://jmlr.org/papers/v25/23-0528.html

#semidefinite #kernels #classification

Efficient Convex Algorithms for Universal Kernel Learning

Published papers at TMLRMay 26, 2023

Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices

Calypso Herrera, Florian Krach, Anastasis Kratsios, Pierre Ruyssen, Josef Teichmann

Action editor: Stephen Becker.

https://openreview.net/forum?id=D45gGvUZp2

#pca #semidefinite #sparse

Denise: Deep Robust Principal Component Analysis for Positive...

The robust PCA of covariance matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse decomposition are...

OpenReview
New Submissions to TMLRFeb 10, 2023

Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices

https://openreview.net/forum?id=D45gGvUZp2

#pca #semidefinite #robust

Denise: Deep Robust Principal Component Analysis for Positive...

The robust PCA of covariance matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse decomposition are...

OpenReview