SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1911–1920 of 3304 papers

TitleStatusHype
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
Neural Decomposition: Functional ANOVA with Variational AutoencodersCode1
A metric on directed graphs and Markov chains based on hitting probabilitiesCode0
Extracting the main trend in a dataset: the Sequencer algorithmCode1
ABID: Angle Based Intrinsic Dimensionality—0
Approximation Algorithms for Sparse Principal Component Analysis—0
On Compression Principle and Bayesian Optimization for Neural Networks—0
Similarity Search with Tensor Core Units—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—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