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 2241–2250 of 3304 papers

TitleStatusHype
Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes—0
This also affects the context - Errors in extraction based summaries—0
T- Hop: Tensor representation of paths in graph convolutional networks—0
Three-body renormalization group limit cycles based on unsupervised feature learning—0
Threshold Strategy for Leaking Corner-Free Hamilton-Jacobi Reachability with Decomposed Computations—0
Tight bounds for learning a mixture of two gaussians—0
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels—0
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals—0
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking—0
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics—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