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 1226–1250 of 3304 papers

TitleStatusHype
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation—0
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information—0
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images—0
A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy—0
An Algorithm and Heuristic based on Normalized Mutual Information for Dimensionality Reduction and Classification of Hyperspectral images—0
NeuroMapper: In-browser Visualizer for Neural Network TrainingCode0
Face Pyramid Vision TransformerCode0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing—0
Transformer-based dimensionality reduction—0
Decentralized State Estimation In A Dimension-Reduced Linear RegressionCode0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
Pinning control of networks: dimensionality reduction through simultaneous block-diagonalization of matrices—0
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration—0
Efficient NTK using Dimensionality Reduction—0
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network—0
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model—0
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning—0
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
Probabilistic partition of unity networks for high-dimensional regression problems—0
Detection and Evaluation of Clusters within Sequential Data—0
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes—0
Benign AutoencodersCode0
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
A Unified Framework for Optimization-Based Graph Coarsening—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