SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2161–2170 of 3304 papers

TitleStatusHype
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
Sufficient Forecasting Using Factor Models—0
Super-Bit Locality-Sensitive Hashing—0
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information—0
Supervised Dimensionality Reduction and Visualization using Centroid-encoder—0
Supervised Dimensionality Reduction via Distance Correlation Maximization—0
Supervised Exponential Family Principal Component Analysis via Convex Optimization—0
Supervised Kernel PCA For Longitudinal Data—0
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons—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