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 2351–2375 of 3304 papers

TitleStatusHype
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Memory-efficient training with streaming dimensionality reduction—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Merging Two Cultures: Deep and Statistical Learning—0
Message-Relevant Dimension Reduction of Neural Populations—0
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
Metastatic Breast Cancer Prognostication Through Multimodal Integration of Dimensionality Reduction Algorithms and Classification Algorithms—0
Metrics for Probabilistic Geometries—0
Microstructure under the Microscope: Tools to Survive and Thrive in The Age of (Too Much) Information—0
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering—0
Minimax-Optimal Dimension-Reduced Clustering for High-Dimensional Nonspherical Mixtures—0
Minimum description length as an objective function for non-negative matrix factorization—0
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations—0
Mixed-Variate Restricted Boltzmann Machines—0
Mixtape Application: Music Map Methodology and Evaluation—0
Mixture Probabilistic Principal Geodesic Analysis—0
MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization—0
MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—0
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems—0
MM Algorithms for Distance Covariance based Sufficient Dimension Reduction and Sufficient Variable Selection—0
Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings—0
Modal Principal Component Analysis—0
Model-based SIR for dimension reduction—0
Show:102550
← PrevPage 95 of 133Next →
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