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 1451–1475 of 3304 papers

TitleStatusHype
Efficient L1-Norm Principal-Component Analysis via Bit Flipping—0
Grammar-based Ordinary Differential Equation Discovery—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation—0
Efficient GPU implementation of randomized SVD and its applications—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Efficient Fair Principal Component Analysis—0
Graph filtering for data reduction and reconstruction—0
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Graph Learning via Spectral Densification—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs—0
Graphon Pooling in Graph Neural Networks—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
Graph Signal Representation with Wasserstein Barycenters—0
Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
GRASPEL: Graph Spectral Learning at Scale—0
Grassmann Averages for Scalable Robust PCA—0
Grassmann Graph Embedding—0
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation—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