SOTAVerified

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 1226–1250 of 3304 papers

TitleStatusHype
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
Uniform Manifold Approximation with Two-phase OptimizationCode1
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Drone Flocking Optimization using NSGA-II and Principal Component Analysis—0
Novel optimized crow search algorithm for feature selectionCode0
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal ParticlesCode0
Local Explanation of Dimensionality ReductionCode0
Representative period selection for power system planning using autoencoder-based dimensionality reduction—0
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
Trainable Compound Activation Functions for Machine Learning—0
Dimension Reduction for time series with Variational AutoEncoders—0
Spherical Rotation Dimension Reduction with Geometric Loss FunctionsCode0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
Exploring Dimensionality Reduction Techniques in Multilingual Transformers—0
A dynamical systems based framework for dimension reduction—0
Diagnosing and Fixing Manifold Overfitting in Deep Generative ModelsCode1
Wassmap: Wasserstein Isometric Mapping for Image Manifold LearningCode0
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
Assessment of convolutional recurrent autoencoder network for learning wave propagation—0
T- Hop: Tensor representation of paths in graph convolutional networks—0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems—0
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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