SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2661–2670 of 3304 papers

TitleStatusHype
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
From which world is your graph?—0
The neighborhood lattice for encoding partial correlations in a Hilbert spaceCode0
Approximation of Functions over Manifolds: A Moving Least-Squares Approach—0
Sampling and multilevel coarsening algorithms for fast matrix approximations—0
Learning Neural Representations of Human Cognition across Many fMRI StudiesCode0
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics—0
Stochastic variance reduced multiplicative update for nonnegative matrix factorization—0
Dimensionality reduction methods for molecular simulations—0
Automatic Differentiation in PyTorchCode0
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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