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 376–400 of 3304 papers

TitleStatusHype
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