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 1901–1950 of 3304 papers

TitleStatusHype
Generalized Categorization Axioms—0
Generalized notions of sparsity and restricted isometry property. Part I: A unified framework—0
Generalized Principal Component Analysis—0
Supervised tensor decomposition with features on multiple modes—0
Generalized two-dimensional linear discriminant analysis with regularization—0
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
Generative Local Metric Learning for Nearest Neighbor Classification—0
Generative Modeling: A Review—0
Generative Modeling for Atmospheric Convection—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting—0
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
Genomics Data Analysis via Spectral Shape and Topology—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees—0
Survey: Geometric Foundations of Data Reduction—0
Geometric Machine Learning on EEG Signals—0
Geometry and dimensionality reduction of feature spaces in primary visual cortex—0
Geometry-Aware Hamiltonian Variational Auto-Encoder—0
Geometry of Deep Learning for Magnetic Resonance Fingerprinting—0
GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems—0
G-invariant diffusion maps—0
GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction—0
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics—0
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies—0
Global explainability of a deep abstaining classifier—0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
Going Beyond T-SNE: Exposing whatlies in Text Embeddings—0
Golden Ratio-Based Sufficient Dimension Reduction—0
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Graceful Forgetting II. Data as a Process—0
Gradient-based kernel method for feature extraction and variable selection—0
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction—0
Gradient-Free Textual Inversion—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model—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
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—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
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
Show:102550
← PrevPage 39 of 67Next →
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