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
Concept Drift Detection in Federated Networked Systems—0
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons—0
On the use of Wasserstein metric in topological clustering of distributional data—0
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectiveCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach—0
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Bubblewrap: Online tiling and real-time flow prediction on neural manifoldsCode0
Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)Code1
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
Convolutional Autoencoders for Reduced-Order Modeling—0
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey—0
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing—0
Joint Characterization of Spatiotemporal Data Manifolds—0
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
M-ar-K-Fast Independent Component AnalysisCode0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs—0
Clustering with UMAP: Why and How Connectivity MattersCode1
Predicting Molecular Phenotypes with Single Cell RNA Sequencing Data: an Assessment of Unsupervised Machine Learning Models—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