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Dimensionality Reduction

706 directly classified papers

Papers per year

Papers

Principal Component Projection Without Principal Component Analysis ICML 2016
Geometric Mean Metric Learning ICML 2016
Spectral Learning of Dynamic Systems from Nonequilibrium Data NIPS 2016
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate ICML 2015
Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis ICML 2015
Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams ICML 2015
Classification with Low Rank and Missing Data ICML 2015
Theory of Dual-sparse Regularized Randomized Reduction ICML 2015
Landmarking Manifolds with Gaussian Processes ICML 2015
Streaming Sparse Principal Component Analysis ICML 2015
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data ICML 2015
Stay on path: PCA along graph paths ICML 2015
Deterministic Independent Component Analysis ICML 2015
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
Heteroscedastic Max-Min Distance Analysis CVPR 2015
Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables CVPR 2015