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 281–290 of 3304 papers

TitleStatusHype
Deep Diffusion MapsCode0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
Latent Manifold Reconstruction and Representation with Topological and Geometrical RegularizationCode0
Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class PredictionCode0
Bayesian full waveform inversion with sequential surrogate model refinement—0
Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
OASIS: Optimized Lightweight Autoencoder System for Distributed In-Sensor computing—0
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup—0
Surrogate to Poincaré inequalities on manifolds for dimension reduction in nonlinear feature spaces—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