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Dimensionality Reduction

706 directly classified papers

Papers per year

Papers

Streaming Kernel Principal Component Analysis AISTATS 2016
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations CVPR 2016
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization CVPR 2016
Robust Tensor Factorization With Unknown Noise CVPR 2016
Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis CVPR 2016
Learning a Discriminative Null Space for Person Re-Identification CVPR 2016
Optimal Sparse Linear Encoders and Sparse PCA NIPS 2016
Single Pass PCA of Matrix Products NIPS 2016
Subspace Learning with Partial Information JMLR 2016
General Tensor Spectral Co-clustering for Higher-Order Data NIPS 2016
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering CVPR 2016
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis NIPS 2016
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis COLING 2016
Multitask Principal Component Analysis ACML 2016
Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness JMLR 2016
Large Margin Discriminant Dimensionality Reduction in Prediction Space NIPS 2016
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition JMLR 2016
Bayesian group factor analysis with structured sparsity JMLR 2016
Online PCA with Optimal Regret JMLR 2016
Megaman: Scalable Manifold Learning in Python JMLR 2016
An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds CVPR 2016
Beyond CCA: Moment Matching for Multi-View Models ICML 2016
Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit ICML 2016
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling ICML 2016
Rich Component Analysis ICML 2016