Kernel Method

Follow this topic Ask about this topic

Recent momentum

+36%

19 papers in the last 28 days · 0.3% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

Weekly history

Recent digests

What was published in this topic, kept on the site without email delivery.

Period ending 2026-09-21

7 new papers

A weekly snapshot of new work published in Kernel Method.

Period ending 2026-09-14

2 new papers

A weekly snapshot of new work published in Kernel Method.

149 papers

Latest in Kernel Method

Browse all topics →

Robust Fuzzy local k-plane clustering with mixture distance of hinge loss and L1 norm
Apr 24, 2026Junjun Huang, Xiliang Lu, Xuelin Xie +1Image Clustering K-Means
Kernel Contracts: A Specification Language for ML Kernel Correctness Across Heterogeneous Silicon
Apr 23, 2026Cooper VeitKernel Contracts Graphics Processing Unit Kernels
A Kernel Nonconformity Score for Multivariate Conformal Prediction
Apr 23, 2026Louis Meyer, Wenkai XuNonconformity Scores Conformal Prediction
Deep kernel video approximation for unsupervised action segmentation
Apr 23, 2026Silvia L. Pintea, Jouke DijkstraTemporal Action Segmentation Unsupervised
LaplacianFormer:Rethinking Linear Attention with Laplacian Kernel
Apr 22, 2026Zhe Feng, Sen Lian, Changwei Wang +5Quadratic Attention Softmax Attention
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Apr 16, 2026Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. HuertasSupport Vector Machines Kernel Method
KVNN: Learnable Multi-Kernel Volterra Neural Networks
Apr 16, 2026Haoyu Yun, Hamid Krim, Yufang BaoNeural Tangent Kernel Neural Network
GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes
Apr 3, 2026Mijeong Kim, Jungtaek Kim, Bohyung HanGaussian Splatting Gaussian Process
Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels
Mar 31, 2026Marcel Tomàs Bernal, Neil Rohit Mallinar, Mikhail BelkinFeature Learning Kernel Method
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Mar 17, 2026Amon Lahr, Anna Scampicchio, Johannes Köhler +1Gaussian Process Kernel Hilbert Spaces
Spectral Hierarchy of the Cosmic Web
Mar 16, 2026Francisco-Shu Kitaura, Francesco SinigagliaCosmology Hierarchical
Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables
Feb 27, 2026Yoichi ChikaharaFairness Interventions Causal Graph
Generative Modeling via Kernelized Stochastic Interpolants
Feb 23, 2026Florentin Coeurdoux, Etienne Lempereur, Nathanaël Cuvelle-Magar +2Stochastic Differential Equations Generative Models
Optimal Learning Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay
Feb 6, 2026Binghui Li, Zilin Wang, Fengling Chen +3Learning Rates Scaling Laws
Minimum distance classification for nonlinear dynamical systems
Jan 7, 2026Dominique MartinezNonlinear Dynamics Qualitative Dynamics
DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
Nov 19, 2025Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4Accelerator Kernel Method
Sublinear Sketches for Approximate Nearest Neighbor and Kernel Density Estimation
Oct 27, 2025Ved Danait, Srijan Das, Sujoy BhoreApproximate Nearest-Neighbor Search Kernel Method
Computationally-efficient Graph Modeling with Refined Graph Random Features
Oct 9, 2025Krzysztof Choromanski, Avinava Dubey, Arijit Sehanobish +1Graph Representation Learning Kernel Method
Data-efficient Kernel Methods for Learning Hamiltonian Systems
Sep 21, 2025Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi +2Hamiltonian Physics Hamiltonian
A Compositional Kernel Model for Feature Learning
Sep 17, 2025Feng Ruan, Keli Liu, Michael JordanKernel Ridge Regression Feature Learning
Shapley-Inspired Feature Weighting in $k$ -means with No Additional Hyperparameters
Aug 11, 2025Richard J. Fawley, Renato Cordeiro de AmorimK-Means Shapley Value
Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences
Jun 20, 2025Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1Kernel Hilbert Spaces Gaussian Process
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation
May 21, 2025Kun Fang, Qinghua Tao, Mingzhen He +6Out-Of-Distribution Detection In-Distribution
Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction
May 10, 2025Michael W. Trosset, Kaiyi Tan, Minh Tang +1Principal Component Analysis Kernel Method
Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design
Feb 16, 2025Nan-Hong Kuo, Renata WongKernel Method Physics-Informed Machine Learning
Classification of Financial Data Using Quantum Support Vector Machine
Dec 14, 2024Seemanta Bhattacharjee, MD. Muhtasim Fuad, A. K. M. Fakhrul HossainQuantum Kernels Kernel Method
Consistent Distributed Ranking of Generative Models via Kernel Distances
Oct 18, 2023Zixiao Wang, Farzan Farnia, Zhenghao Lin +2Late-Stage Ranker Model Evaluation
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
Dec 23, 2018Balázs Csanád Csáji, Krisztián Balázs KisReliable Uncertainty Quantification Kernel Method
Classical and quantum kernel fusion for two-sample testing
Date pendingYu Terada, Yugo Ogio, Ken Arai +2Quantum Kernels Two-Sample Testing

Apr 24, 2026 · cs.LGJ/K move · Enter open · S save

Robust Fuzzy local k-plane clustering with mixture distance of hinge loss and L1 norm

Junjun Huang, Xiliang Lu, Xuelin Xie, Jerry Zhijian Yang

China Electric Power Research Institute, Wuhan 430074, China, and also with the Key Laboratory of Measurement and Test of High Voltage and Heavy Current, State Administration for Market Regulation, Wuhan 430074, China · School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China, the National Center for Applied Mathematics in Hubei, Wuhan University, Wuhan 430072, China, and also with the Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, China · National Center for Applied Mathematics in Hubei, Wuhan University, Wuhan 430072, China, the Wuhan Institute for Math and AI, Wuhan University, Wuhan 430072, China, the School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China, and the Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, China

K-plane clustering (KPC), hyperplane clustering, and mixture regression all essentially fall within the same class of problems. This problem can be conceptualized as clustering in relatively high-dimensional K subspaces or K linear manifolds. Traditional KPC or fuzzy KPC models demonstrate a pronounced susceptibility to outliers, as they presuppose that the projection distance between data points and the plane normal vector adheres to the L2 distance. Meanwhile, the assumption of infinitely extending clusters adversely affects clustering performance. To solve these problems, this paper proposed a new robust fuzzy local k-plane clustering (RFLkPC) method that combines the mixture distance of hinge loss and L1 norm. The RFLkPC model assumes that each plane cluster is bounded to a finite area, which can flexibly and robustly handle plane clustering tasks with outliers or not. The corresponding model and optimization algorithms of RFLkPC were provided. Compared to other related models on this topic, a large number of experiments verify the efficiency of RFLkPC on simulated data and real data. The source code for the proposed RFLkPC method is publicly available at https://github.com/xuelin-xie/RFLkPC.

Image Clustering K-Means Kernel Method Outliers Mixture Models Loss Function

Open paper Ask about it PDF