SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1521–1530 of 3304 papers

TitleStatusHype
Computational Graph Completion—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
Dimensionality Reduction for Wasserstein Barycenter—0
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
Persuasion by Dimension Reduction—0
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—0
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model—0
SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network—0
Unsupervised Data-Driven Nuclei Segmentation For Histology Images—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