SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 326350 of 3304 papers

TitleStatusHype
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal EmbeddingsCode0
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot LayoutsCode0
Accelerated Stochastic Power IterationCode0
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition ManifoldsCode0
A journey in ESN and LSTM visualisations on a language taskCode0
ADAGIO: Fast Data-aware Near-Isometric Linear EmbeddingsCode0
Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic DischargesCode0
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting MachineCode0
Distributed Lyapunov Functions for Nonlinear NetworksCode0
Dimension Reduction and MARSCode0
Reducing the dimensionality of data using tempered distributionsCode0
Dimensionality Reduction using Similarity-induced EmbeddingsCode0
Dimension-reduced Optimization of Multi-zone Thermostatically Controlled LoadsCode0
Dimension Reduction for Data with Heterogeneous MissingnessCode0
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanismsCode0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
Active Learning for Manifold Gaussian Process RegressionCode0
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Dimensionality reduction, regularization, and generalization in overparameterized regressionsCode0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
DiffRed: Dimensionality Reduction guided by stable rankCode0
Differentiable VQ-VAE's for Robust White Matter Streamline EncodingsCode0
Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon ForecastingCode0
Detecting Adversarial Examples through Nonlinear Dimensionality ReductionCode0
Designing Illuminant Spectral Power Distributions for Surface ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified