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 2511–2520 of 3304 papers

TitleStatusHype
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
A Comprehensive Filter Feature Selection for Improving Document Classification—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
A convex formulation for high-dimensional sparse sliced inverse regression—0
A Convex formulation for linear discriminant analysis—0
A Convex Formulation for Spectral Shrunk Clustering—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