SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 231–240 of 3304 papers

TitleStatusHype
Active Learning for Manifold Gaussian Process RegressionCode0
Distributed Lyapunov Functions for Nonlinear NetworksCode0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—0
Local Averaging Accurately Distills Manifold Structure From Noisy Data—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques—0
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—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