SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 131–140 of 3304 papers

TitleStatusHype
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
EigenGS Representation: From Eigenspace to Gaussian Image Space—0
Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation—0
Robust Multilinear Principal Component Analysis—0
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI TractographyCode0
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing—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