kernel principal component analysis

Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data JMLR 2026 A Reproduction Study: The Kernel PCA Interpretation of Self-Attention Fails Under Scrutiny ACL 2025 Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method ICML 2024 Spectrally Transformed Kernel Regression ICLR 2024 Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis NeurIPS 2024 Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms ICML 2023 Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence JMLR 2022 Kernel Methods for Radial Transformed Compositional Data with Many Zeros ICML 2022 Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA JMLR 2022 Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation EMNLP 2020 Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020 Autoencoding any Data through Kernel Autoencoders AISTATS 2019 Improving Word Embeddings Using Kernel PCA ACL 2019 Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA JMLR 2019 Multi-scale Nystrom Method AISTATS 2018 Automatic Detection of Parkinson’s Disease Based on Modulated Vowels Interspeech 2016 Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes JMLR 2016 Streaming Kernel Principal Component Analysis AISTATS 2016 Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients NeurIPS 2015 Kernel Mean Estimation via Spectral Filtering NeurIPS 2014 Subspace Embeddings for the Polynomial Kernel NeurIPS 2014 Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices CVPR 2013 On One-Shot Similarity Kernels: Explicit Feature Maps and Properties ICCV 2013 A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis JMLR 2011 Avoiding False Positive in Multi-Instance Learning NeurIPS 2010