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 1081–1090 of 3304 papers

TitleStatusHype
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Canonical Variates in Wasserstein Metric Space—0
Canonical Correlation Analysis of Datasets with a Common Source Graph—0
A Neural Operator-Based Emulator for Regional Shallow Water Dynamics—0
Can Genetic Programming Do Manifold Learning Too?—0
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
A Neural Network Transformer Model for Composite Microstructure Homogenization—0
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction—0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—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