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Dimensionality Reduction

706 directly classified papers

Papers per year

Papers

Projective Low-rank Subspace Clustering via Learning Deep Encoder IJCAI 2017
Diffusion Approximations for Online Principal Component Estimation and Global Convergence NIPS 2017
Improved Graph Laplacian via Geometric Self-Consistency NIPS 2017
Matrix Norm Estimation from a Few Entries NIPS 2017
Kernel Feature Selection via Conditional Covariance Minimization NIPS 2017
Stochastic Approximation for Canonical Correlation Analysis NIPS 2017
Near Optimal Sketching of Low-Rank Tensor Regression NIPS 2017
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations NIPS 2017
Fitting Low-Rank Tensors in Constant Time NIPS 2017
Approximation Algorithms for $\ell_0$-Low Rank Approximation NIPS 2017
Representing Sentences as Low-Rank Subspaces ACL 2017
Functional Principal Component Analysis of Vocal Tract Area Functions INTERSPEECH 2017
Adaptive Hypergraph Learning for Unsupervised Feature Selection IJCAI 2017
Angle Principal Component Analysis IJCAI 2017
Two dimensional Large Margin Nearest Neighbor for Matrix Classification IJCAI 2017
Flexible Orthogonal Neighborhood Preserving Embedding IJCAI 2017
Quantifying the Informativeness of Similarity Measurements JMLR 2017
Robust Discriminative Clustering with Sparse Regularizers JMLR 2017
Kernel Partial Least Squares for Stationary Data JMLR 2017
Dimension Estimation Using Random Connection Models JMLR 2017
Adaptive Randomized Dimension Reduction on Massive Data JMLR 2017
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach IJCAI 2017
Fast Recursive Low-rank Tensor Learning for Regression IJCAI 2017
Sublinear Time Orthogonal Tensor Decomposition NIPS 2016
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity NIPS 2016