Francesco Tonin
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View article: Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method Open
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) inherently relates to asymmetric kernels and Asymmetric Kernel Sin…
View article: Deep Kernel Principal Component Analysis for multi-level feature learning
Deep Kernel Principal Component Analysis for multi-level feature learning Open
View article: Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers Open
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 Vector Machines (LSSVM) to be combined into a deep architecture…
View article: Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms Open
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 multiple objective functions and leads to efficient gradient-b…
View article: Deep Kernel Principal Component Analysis for Multi-level Feature Learning
Deep Kernel Principal Component Analysis for Multi-level Feature Learning Open
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 deep learning tools have achieved great empirical success, …
View article: Tensor-Based Multi-View Spectral Clustering Via Shared Latent Space
Tensor-Based Multi-View Spectral Clustering Via Shared Latent Space Open
View article: Tensor-based Multi-view Spectral Clustering via Shared Latent Space
Tensor-based Multi-view Spectral Clustering via Shared Latent Space Open
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 overlook model interpretability and exploration of clustering res…
View article: Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints Open
View article: Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine Open
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 detectors has focused on the softmax confidence score from a …