SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2761–2770 of 3304 papers

TitleStatusHype
Designing Illuminant Spectral Power Distributions for Surface ClassificationCode0
Generalized notions of sparsity and restricted isometry property. Part I: A unified framework—0
CatBoost: unbiased boosting with categorical featuresCode0
Efficient Manifold and Subspace Approximations with SphereletsCode0
Improving text classification with vectors of reduced precisionCode0
Dimensionality Reduction using Similarity-induced EmbeddingsCode0
Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network—0
Hidden Talents of the Variational AutoencoderCode0
A survey of dimensionality reduction techniques based on random projection—0
Embedding Feature Selection for Large-scale Hierarchical Classification—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