3 papers
cs.LG2023
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
Francesco Tonin, Alex Lambert, Panagiotis Patrinos +1
The goal of this paper is to revisit Kernel Principal Component Analysis (KPCA) through dualization of a difference of convex functions. This allows to naturally extend KPCA to mul…
cs.LG2023
Deep Kernel Principal Component Analysis for Multi-level Feature Learning
Francesco Tonin, Qinghua Tao, Panagiotis Patrinos +1
Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern…
cs.LG2022
Tensor-based Multi-view Spectral Clustering via Shared Latent Space
Qinghua Tao, Francesco Tonin, Panagiotis Patrinos +1
Multi-view Spectral Clustering (MvSC) attracts increasing attention due to diverse data sources. However, most existing works are prohibited in out-of-sample predictions and overlo…