SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 551–560 of 3304 papers

TitleStatusHype
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
A canonical correlation-based framework for performance analysis of radio access networks—0
A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection—0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
A Graph Based Raman Spectral Processing Technique for Exosome Classification—0
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting—0
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images—0
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction—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