5 citations · 9 across the 12 of their papers we have counts for
12 papers · 1 filter
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
Qinghua Tao, Francesco Tonin, Alex Lambert +3
In contrast with Mercer kernel-based approaches as used e.g., in Kernel Principal Component Analysis (KPCA), it was previously shown that Singular Value Decomposition (SVD) inheren…
HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity
Sonny Achten, Zander Op de Beeck, Francesco Tonin +2
Clustering nodes in heterophilous graphs is challenging as traditional methods assume that effective clustering is characterized by high intra-cluster and low inter-cluster connect…
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
Yingyi Chen, Qinghua Tao, Francesco Tonin +1
While the great capability of Transformers significantly boosts prediction accuracy, it could also yield overconfident predictions and require calibrated uncertainty estimation, wh…
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method
Qinghua Tao, Francesco Tonin, Panagiotis Patrinos +1
Asymmetric data naturally exist in real life, such as directed graphs. Different from the common kernel methods requiring Mercer kernels, this paper tackles the asymmetric kernel-b…
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…
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers
Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
In the context of deep learning with kernel machines, the deep Restricted Kernel Machine (DRKM) framework allows multiple levels of kernel PCA (KPCA) and Least-Squares Support Vect…
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
Yingyi Chen, Qinghua Tao, Francesco Tonin +1
Recently, a new line of works has emerged to understand and improve self-attention in Transformers by treating it as a kernel machine. However, existing works apply the methods for…
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…
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
Sonny Achten, Francesco Tonin, Panagiotis Patrinos +1
We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce uns…
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…
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine
Francesco Tonin, Arun Pandey, Panagiotis Patrinos +1
Detecting out-of-distribution (OOD) samples is an essential requirement for the deployment of machine learning systems in the real world. Until now, research on energy-based OOD de…
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens
We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations. We propose augmenting the original deep restricted kernel machine…