SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 3304 papers

TitleStatusHype
Statistical tuning of artificial neural network—0
Self-Supervised Graph Embedding Clustering—0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language ModelsCode0
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
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
Optimal Projections for Classification with Naive Bayes—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
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
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction AttackCode0
Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays—0
A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model for Variable Terrain Conditions—0
On latent dynamics learning in nonlinear reduced order modeling—0
PAGE: Parametric Generative Explainer for Graph Neural NetworkCode0
An Embedding is Worth a Thousand Noisy LabelsCode1
ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms—0
Robust spectral clustering with rank statistics—0
OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data—0
Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment AnalysisCode0
"Normalized Stress" is Not Normalized: How to Interpret Stress CorrectlyCode0
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling—0
Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines—0
Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics—0
Towards aerodynamic surrogate modeling based on β-variational autoencodersCode0
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions—0
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds—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