dimension reduction

Landmark Ordinal Embedding NeurIPS 2019 Large-scale optimal transport map estimation using projection pursuit NeurIPS 2019 MFPCA: Multiscale Functional Principal Component Analysis AAAI 2019 On Geometric Alignment in Low Doubling Dimension AAAI 2019 RS3CIS: Robust Single-Step Spectral Clustering with Intrinsic Subspace AAAI 2019 Selecting the independent coordinates of manifolds with large aspect ratios NeurIPS 2019 A Scalable Heterogeneous Parallel SOM Based on MPI/CUDA ACML 2018 Fast Randomized PCA for Sparse Data ACML 2018 Fast Similarity Search via Optimal Sparse Lifting NeurIPS 2018 Feature-correlation-aware Gaussian Process Latent Variable Model ACML 2018 Fully Understanding The Hashing Trick NeurIPS 2018 Iterative Supervised Principal Components AISTATS 2018 On b-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data JMLR 2018 Optimal Bounds for Johnson-Lindenstrauss Transformations JMLR 2018 Adaptive Randomized Dimension Reduction on Massive Data JMLR 2017 Clustering with Hidden Markov Model on Variable Blocks JMLR 2017 Fast Network Embedding Enhancement via High Order Proximity Approximation IJCAI 2017 Kernel Feature Selection via Conditional Covariance Minimization NeurIPS 2017 Learning Neural Representations of Human Cognition across Many fMRI Studies NeurIPS 2017 Projective Low-rank Subspace Clustering via Learning Deep Encoder IJCAI 2017 Recovering PCA and Sparse PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements JMLR 2017 Whitening-Free Least-Squares Non-Gaussian Component Analysis ACML 2017 Exponential Family Embeddings NeurIPS 2016 Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking AISTATS 2016 Approximating Sparse PCA from Incomplete Data NeurIPS 2015