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 51–60 of 3304 papers

TitleStatusHype
TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks—0
Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in ChildrenCode0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
Deep Diffusion MapsCode0
Latent Manifold Reconstruction and Representation with Topological and Geometrical RegularizationCode0
Bayesian full waveform inversion with sequential surrogate model refinement—0
Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class PredictionCode0
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
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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