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 351–375 of 3304 papers

TitleStatusHype
Statistical tuning of artificial neural network—0
Self-Supervised Graph Embedding Clustering—0
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language ModelsCode0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Shape-informed surrogate models based on signed distance function domain encoding—0
User-friendly Foundation Model Adapters for Multivariate Time Series Classification—0
The Impact of Element Ordering on LM Agent PerformanceCode0
Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—0
Cost-informed dimensionality reduction for structural digital twin technologies—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
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
Optimal Projections for Classification with Naive Bayes—0
Representational Analysis of Binding in Language Models—0
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models—0
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning—0
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration—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