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 2376–2400 of 3304 papers

TitleStatusHype
Model-based targeted dimensionality reduction for neuronal population data—0
Model-Coupled Autoencoder for Time Series Visualisation—0
Model-Free Context-Aware Word Composition—0
Model-free Vehicle Rollover Prevention: A Data-driven Predictive Control Approach—0
Modeling coherence in ESOL learner texts—0
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks—0
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks—0
Modelling matrix time series via a tensor CP-decomposition—0
Model Order Reduction based on Runge-Kutta Neural Network—0
Modified Multidimensional Scaling and High Dimensional Clustering—0
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—0
MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation—0
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps—0
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets—0
Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning—0
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder—0
MPAD: A New Dimension-Reduction Method for Preserving Nearest Neighbors in High-Dimensional Vector Search—0
MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework—0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Multiclass spectral feature scaling method for dimensionality reduction—0
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing—0
Generating semantic maps through multidimensional scaling: linguistic applications and theory—0
Multidimensional Scaling for Gene Sequence Data with Autoencoders—0
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations—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