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 1201–1225 of 3304 papers

TitleStatusHype
FRE: A Fast Method For Anomaly Detection And Segmentation—0
Kernel PCA for multivariate extremes—0
A Generalized EigenGame with Extensions to Multiview Representation LearningCode0
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant AnalysisCode0
Graceful Forgetting II. Data as a Process—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction—0
Data Dimension Reduction makes ML Algorithms efficient—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—0
Topology of cognitive maps—0
Inverse Kernel DecompositionCode0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Genomics Data Analysis via Spectral Shape and Topology—0
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information—0
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General RelativityCode0
Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines—0
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information—0
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier—0
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation—0
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images—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