SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1601–1610 of 3304 papers

TitleStatusHype
Finding Real-World Orbital Motion Laws from Data—0
Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis—0
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest—0
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer—0
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines—0
Improving evaluation and optimization of MT systems against MEANT—0
Computational Graph Completion—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—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