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 731–740 of 3304 papers

TitleStatusHype
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach—0
Uncovering Temporal Patterns in Visualizations of High-Dimensional Data—0
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons—0
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation ModelsCode0
Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos—0
Assessing the similarity of real matrices with arbitrary shapeCode0
S+t-SNE -- Bringing Dimensionality Reduction to Data StreamsCode0
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data—0
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—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