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 841–850 of 3304 papers

TitleStatusHype
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—0
Vision Transformer with Attention Map Hallucination and FFN Compaction—0
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction—0
Enhanced Sampling with Machine Learning: A Review—0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
Bayesian Non-linear Latent Variable Modeling via Random Fourier FeaturesCode0
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High DimensionsCode0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
G-invariant diffusion maps—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