Dimensionality Reduction

A fast, universal algorithm to learn parametric nonlinear embeddings NeurIPS 2015 A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks NeurIPS 2015 Approximating Sparse PCA from Incomplete Data NeurIPS 2015 A Reduced-Dimension fMRI Shared Response Model NeurIPS 2015 A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate ICML 2015 A Supervised Low-Rank Method for Learning Invariant Subspaces ICCV 2015 Autoencoder Trees ACML 2015 Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) NeurIPS 2015 Bilinear Random Projections for Locality-Sensitive Binary Codes CVPR 2015 Classification with Low Rank and Missing Data ICML 2015 Compressive spectral embedding: sidestepping the SVD NeurIPS 2015 Deterministic Independent Component Analysis ICML 2015 Diversity-Induced Multi-View Subspace Clustering CVPR 2015 Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables CVPR 2015 Fast Orthogonal Projection Based on Kronecker Product ICCV 2015 Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis ICML 2015 Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices ACML 2015 Hashing With Binary Autoencoders CVPR 2015 Heteroscedastic Max-Min Distance Analysis CVPR 2015 Landmarking Manifolds with Gaussian Processes ICML 2015 Local High-Order Regularization on Data Manifolds CVPR 2015 Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation NeurIPS 2015 Membership Representation for Detecting Block-Diagonal Structure in Low-Rank or Sparse Subspace Clustering CVPR 2015 Principal Differences Analysis: Interpretable Characterization of Differences between Distributions NeurIPS 2015 Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric NeurIPS 2015