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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1076–1100 of 3304 papers

TitleStatusHype
Pinning control of networks: dimensionality reduction through simultaneous block-diagonalization of matrices—0
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration—0
ParaDime: A Framework for Parametric Dimensionality ReductionCode1
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
A Unified Framework for Optimization-Based Graph Coarsening—0
Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI—0
Benign AutoencodersCode0
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
Identifying Selections Operating on HIV-1 Reverse Transcriptase via Uniform Manifold Approximation and ProjectionCode0
On The Relative Error of Random Fourier Features for Preserving Kernel Distance—0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
A canonical correlation-based framework for performance analysis of radio access networks—0
Patients' Severity States Classification based on Electronic Health Record (EHR) Data using Multiple Machine Learning and Deep Learning ApproachesCode0
Spectral Diffusion Processes—0
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach—0
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature DescriptorsCode1
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)—0
On Projections to Linear SubspacesCode0
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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