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 576–600 of 3304 papers

TitleStatusHype
Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing—0
HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions—0
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems—0
Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection—0
Relevance for Human Robot Collaboration—0
TopoMap++: A faster and more space efficient technique to compute projections with topological guarantees—0
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Applications of machine learning to predict seasonal precipitation for East Africa—0
The Surprising Robustness of Partial Least Squares—0
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models—0
Representational Analysis of Binding in Language Models—0
Optimal Projections for Classification with Naive Bayes—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration—0
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning—0
Nearest Neighbor CCP-Based Molecular Sequence Analysis—0
A semi-supervised learning using over-parameterized regression—0
Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications—0
Application Research On Real-Time Perception Of Device Performance Status—0
The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines—0
Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging—0
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine—0
Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction AttackCode0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays—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