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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1951–1975 of 3304 papers

TitleStatusHype
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political PartiesCode0
Attention or memory? Neurointerpretable agents in space and time—0
Linear Tensor Projection Revealing Nonlinearity—0
Manifold Learning via Manifold Deflation—0
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology PatchesCode0
Generative Modeling for Atmospheric Convection—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
Deep neural networks for the evaluation and design of photonic devices—0
Subspace approximation with outliers—0
VAE-KRnet and its applications to variational Bayes—0
Local Neighbor Propagation Embedding—0
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
A metric on directed graphs and Markov chains based on hitting probabilitiesCode0
Approximation Algorithms for Sparse Principal Component Analysis—0
ABID: Angle Based Intrinsic Dimensionality—0
On Compression Principle and Bayesian Optimization for Neural Networks—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
Similarity Search with Tensor Core Units—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN featuresCode0
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Estimating Model Uncertainty of Neural Networks in Sparse Information Form—0
Training (Overparametrized) Neural Networks in Near-Linear Time—0
Weakly-correlated synapses promote dimension reduction in deep neural networks—0
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding DistributionCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified