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dimensionality reduction

720 papers

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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

Noisy L0-sparse subspace clustering on dimensionality reduced data UAI 2022
Learning Contrastive Embedding in Low-Dimensional Space NIPS 2022
Fast and Compact Bilinear Pooling by Shifted Random Maclaurin AAAI 2021
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized Autoencoders JMLR 2021
Entangled Kernels - Beyond Separability JMLR 2021
Low-Rank Registration Based Manifolds for Convection-Dominated PDEs AAAI 2021
Fixed-Parameter and Approximation Algorithms for PCA with Outliers ICML 2021
GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data IJCAI 2021
Subspace Clustering through Sub-Clusters JMLR 2021
High-dimensional Experimental Design and Kernel Bandits ICML 2021
Unfolding-Model-Based Visualization: Theory, Method and Applications JMLR 2021
Product Manifold Learning AISTATS 2021
LDLE: Low Distortion Local Eigenmaps JMLR 2021
Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing COLT 2021
Bringing UMAP Closer to the Speed of Light with GPU Acceleration AAAI 2021
Geometric structure of graph Laplacian embeddings JMLR 2021
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections ICML 2021
Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing AAAI 2021
Self-Paced Two-dimensional PCA AAAI 2021
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding ICML 2021
Quantization Algorithms for Random Fourier Features ICML 2021
Binary Matrix Factorisation via Column Generation AAAI 2021
BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders ICML 2021
Principal Bit Analysis: Autoencoding with Schur-Concave Loss ICML 2021
Guided Visual Exploration of Relations in Data Sets JMLR 2021