5 papers
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…
Accelerating Spectral Clustering under Fairness Constraints
Francesco Tonin, Alex Lambert, Johan A. K. Suykens +1
Fairness of decision-making algorithms is an increasingly important issue. In this paper, we focus on spectral clustering with group fairness constraints, where every demographic g…
Nonlinear functional regression by functional deep neural network with kernel embedding
Zhongjie Shi, Jun Fan, Linhao Song +2
Recently, deep learning has been widely applied in functional data analysis (FDA) with notable empirical success. However, the infinite dimensionality of functional data necessitat…
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…
Generative Kernel Spectral Clustering
David Winant, Sonny Achten, Johan A. K. Suykens
Modern clustering approaches often trade interpretability for performance, particularly in deep learning-based methods. We present Generative Kernel Spectral Clustering (GenKSC), a…