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 2381–2390 of 3304 papers

TitleStatusHype
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
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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