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 1961–1970 of 3304 papers

TitleStatusHype
Local Neighbor Propagation Embedding—0
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
A metric on directed graphs and Markov chains based on hitting probabilitiesCode0
Approximation Algorithms for Sparse Principal Component Analysis—0
ABID: Angle Based Intrinsic Dimensionality—0
On Compression Principle and Bayesian Optimization for Neural Networks—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—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
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN featuresCode0
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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