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 2451–2475 of 3304 papers

TitleStatusHype
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
Network Distance Based on Laplacian Flows on Graphs—0
Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments—0
Neural-Kernelized Conditional Density Estimation—0
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval—0
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture—0
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds—0
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps—0
Neuroscience-inspired online unsupervised learning algorithms—0
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance—0
News2vec: News Network Embedding with Subnode Information—0
New Transfer Learning Techniques for Disparate Label Sets—0
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images—0
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection—0
Noise-robust latent vector reconstruction in ptychography using deep generative models—0
Noisy Data Visualization using Functional Data Analysis—0
Noisy multi-label semi-supervised dimensionality reduction—0
NOMAD Projection—0
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals—0
Non-Euclidean Self-Organizing Maps—0
Non-Gaussian Component Analysis using Entropy Methods—0
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis—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