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Dimensionality Reduction

707 papers

Papers per year

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Papers

Megaman: Scalable Manifold Learning in Python JMLR 2016
Online PCA with Optimal Regret JMLR 2016
Bayesian group factor analysis with structured sparsity JMLR 2016
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition JMLR 2016
A fast, universal algorithm to learn parametric nonlinear embeddings NIPS 2015
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition NIPS 2015
Sparse PCA via Bipartite Matchings NIPS 2015
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients NIPS 2015
Space-Time Local Embeddings NIPS 2015
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks NIPS 2015
Approximating Sparse PCA from Incomplete Data NIPS 2015
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation NIPS 2015
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) NIPS 2015
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric NIPS 2015
Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices ACML 2015
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities ACML 2015
Autoencoder Trees ACML 2015
Sparse Submodular Probabilistic PCA AISTATS 2015
Hashing With Binary Autoencoders CVPR 2015
Diversity-Induced Multi-View Subspace Clustering CVPR 2015
Bilinear Random Projections for Locality-Sensitive Binary Codes CVPR 2015
Membership Representation for Detecting Block-Diagonal Structure in Low-Rank or Sparse Subspace Clustering CVPR 2015
Semi-Supervised Learning With Explicit Relationship Regularization CVPR 2015
Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables CVPR 2015
Heteroscedastic Max-Min Distance Analysis CVPR 2015