SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 351–360 of 3304 papers

TitleStatusHype
Dynamical Mode Recognition of Turbulent Flames in a Swirl-stabilized Annular Combustor by a Time-series Learning Approach—0
Stabilization Analysis and Mode Recognition of Kerosene Supersonic Combustion: A Deep Learning Approach Based on Res-CNN-beta-VAE—0
Latent Space Representation of Electricity Market Curves for Improved Prediction Efficiency—0
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning TasksCode0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data—0
Cover Learning for Large-Scale Topology Representation—0
A Novel Approach for Intrinsic Dimension Estimation—0
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality ReductionCode0
Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States—0
Robust Unsupervised Fault Diagnosis For High-Dimensional Nonlinear Noisy Data—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