← Keywords

dimensionality reduction

720 papers

Explore in graph

Also known as

SDR PCA DR

Co-occurring keywords

manifold learning (402) principal component analysis (316) feature extraction (1578) random projection (145) unsupervised learning (3255) representation learning (6174) kernel methods (1096) metric learning (1189) feature selection (649) matrix factorization (529)

Papers

Dimension Estimation Using Random Connection Models JMLR 2017
Locality Preserving Projections for Grassmann manifold IJCAI 2017
Robust Discriminative Clustering with Sparse Regularizers JMLR 2017
Distance Covariance Analysis AISTATS 2017
Quantifying the Informativeness of Similarity Measurements JMLR 2017
From which world is your graph NIPS 2017
Near Optimal Sketching of Low-Rank Tensor Regression NIPS 2017
Flexible Orthogonal Neighborhood Preserving Embedding IJCAI 2017
Simple strategies for recovering inner products from coarsely quantized random projections NIPS 2017
Nearly Isometric Embedding by Relaxation NIPS 2016
Megaman: Scalable Manifold Learning in Python JMLR 2016
Model-based Co-clustering for High Dimensional Sparse Data AISTATS 2016
Unsupervised Feature Selection by Preserving Stochastic Neighbors AISTATS 2016
Online PCA with Optimal Regret JMLR 2016
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations CVPR 2016
Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis CVPR 2016
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity NIPS 2016
Dimensionality Reduction of Massive Sparse Datasets Using Coresets NIPS 2016
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated NIPS 2016
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes JMLR 2016
Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces AISTATS 2016
Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness JMLR 2016
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives COLING 2016
The Variational Nystrom method for large-scale spectral problems ICML 2016
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA ICML 2016