SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 531–540 of 3304 papers

TitleStatusHype
Assessing the similarity of real matrices with arbitrary shapeCode0
S+t-SNE -- Bringing Dimensionality Reduction to Data StreamsCode0
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data—0
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking SystemsCode1
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer CompressionCode1
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
A Wasserstein perspective of Vanilla GANs—0
Text Clustering with Large Language Model Embeddings—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