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Dimensionality Reduction

706 directly classified papers

Papers per year

Papers

Understanding Sparse JL for Feature Hashing NIPS 2019
Multi-Criteria Dimensionality Reduction with Applications to Fairness NIPS 2019
Analytical Methods for Interpretable Ultradense Word Embeddings EMNLP 2019
Quantifying the Semantic Core of Gender Systems EMNLP 2019
Learning nonlinear level sets for dimensionality reduction in function approximation NIPS 2019
Low Permutation-rank Matrices: Structural Properties and Noisy Completion JMLR 2019
Scalable Interpretable Multi-Response Regression via SEED JMLR 2019
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis JMLR 2019
Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering ICCV 2019
Dimensionality reduction: theoretical perspective on practical measures NIPS 2019
Spectral Feature Scaling Method for Supervised Dimensionality Reduction IJCAI 2018
A Biresolution Spectral Framework for Product Quantization CVPR 2018
Learning Low-Dimensional Temporal Representations ICML 2018
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods COLT 2018
Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering IJCAI 2018
A Scalable Heterogeneous Parallel SOM Based on MPI/CUDA ACML 2018
Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification ACML 2018
Cascaded Low Rank and Sparse Representation on Grassmann Manifolds IJCAI 2018
Out-of-sample extension of graph adjacency spectral embedding ICML 2018
Streaming Principal Component Analysis in Noisy Setting ICML 2018
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering ICML 2018
Simple Classification Using Binary Data JMLR 2018
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models JMLR 2018
A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning JMLR 2018
HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data JMLR 2018