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Dimensionality Reduction

707 papers

Papers per year

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Papers

Learning Low-Dimensional Temporal Representations ICML 2018
Finding Frequent Entities in Continuous Data IJCAI 2018
Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering IJCAI 2018
Spectral Feature Scaling Method for Supervised Dimensionality Reduction IJCAI 2018
Cascaded Low Rank and Sparse Representation on Grassmann Manifolds IJCAI 2018
HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data JMLR 2018
A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning JMLR 2018
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models JMLR 2018
Simple Classification Using Binary Data JMLR 2018
Diffusion Approximations for Online Principal Component Estimation and Global Convergence NIPS 2017
Cross-Spectral Factor Analysis 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
Principal Variety Analysis CORL 2017
Representing Sentences as Low-Rank Subspaces ACL 2017
Compact Matrix Factorization With Dependent Subspaces CVPR 2017
Additive Component Analysis CVPR 2017
Scalable Person Re-Identification on Supervised Smoothed Manifold CVPR 2017
Subspace Clustering via Variance Regularized Ridge Regression CVPR 2017