SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2491–2500 of 3304 papers

TitleStatusHype
Non-Negative Matrix Factorization with Scale Data Structure Preservation—0
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction—0
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging—0
Non-Orthogonal Explicit Semantic Analysis—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Non-PSD Matrix Sketching with Applications to Regression and Optimization—0
Non-Redundant Spectral Dimensionality Reduction—0
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis—0
Notes on Low-rank Matrix Factorization—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