SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1831–1840 of 3304 papers

TitleStatusHype
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Parametric UMAP embeddings for representation and semi-supervised learningCode3
Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)—0
Deep Learning of Individual AestheticsCode1
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality ReductionCode1
Compact Learning for Multi-Label Classification—0
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and SurveyCode0
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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