Dimensionality Reduction

Gaussian and Wishart Hyperkernels NeurIPS 2006 Graph Laplacian Regularization for Large-Scale Semidefinite Programming NeurIPS 2006 Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons NeurIPS 2006 In-Network PCA and Anomaly Detection NeurIPS 2006 In Search of Non-Gaussian Components of a High-Dimensional Distribution JMLR 2006 Large Margin Component Analysis NeurIPS 2006 Learning to Traverse Image Manifolds NeurIPS 2006 Manifold Denoising NeurIPS 2006 MLLE: Modified Locally Linear Embedding Using Multiple Weights NeurIPS 2006 New Algorithms for Efficient High-Dimensional Nonparametric Classification JMLR 2006 Nonnegative Sparse PCA NeurIPS 2006 Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension NeurIPS 2006 Sparse Kernel Orthonormalized PLS for feature extraction in large data sets NeurIPS 2006 Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds NeurIPS 2006 Towards a general independent subspace analysis NeurIPS 2006 Unsupervised Regression with Applications to Nonlinear System Identification NeurIPS 2006 Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems JMLR 2005 On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning JMLR 2005 Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces JMLR 2004 Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces JMLR 2004 Efficient Feature Selection via Analysis of Relevance and Redundancy JMLR 2004 Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters JMLR 2004 An Introduction to Variable and Feature Selection JMLR 2003 Beyond Independent Components: Trees and Clusters JMLR 2003 Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation JMLR 2003