SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1441–1450 of 3304 papers

TitleStatusHype
Dimension Reduction for Data with Heterogeneous MissingnessCode0
Non-Euclidean Self-Organizing Maps—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?Code0
Weighted Low Rank Matrix Approximation and Acceleration—0
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in ConnectomicsCode0
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction—0
Machine-Learned HASDM Model with Uncertainty Quantification—0
A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline TimepointCode0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—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