SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1451–1500 of 3304 papers

TitleStatusHype
Efficient Large-Scale Similarity Search Using Matrix Factorization—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
Bridging Dense and Sparse Maximum Inner Product Search—0
An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection—0
Efficient L1-Norm Principal-Component Analysis via Bit Flipping—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
Efficient GPU implementation of randomized SVD and its applications—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
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering—0
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
A Probabilistic Graph Coupling View of Dimension Reduction—0
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction—0
GroupEnc: encoder with group loss for global structure preservation—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Group Preserving Label Embedding for Multi-Label Classification—0
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction—0
Γ-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data—0
Hand Gesture Recognition with Leap Motion—0
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Efficient Fair Principal Component Analysis—0
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems—0
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions—0
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring—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