SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2021–2030 of 3304 papers

TitleStatusHype
Hybrid Subspace Learning for High-Dimensional Data—0
Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with Physics-informed Residual Connected Neural Operator—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier—0
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information—0
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition—0
Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution—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