SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2611–2620 of 3304 papers

TitleStatusHype
A Low Effort Approach to Structured CNN Design Using PCA—0
Alternating Co-Quantization for Cross-Modal Hashing—0
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction—0
Alternative Channel Charting Techniques in Cellular Wireless Communications—0
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data—0
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems—0
A mechanism-driven reinforcement learning framework for shape optimization of airfoils—0
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
A Method for Classifying Snow Using Ski-Mounted Strain Sensors—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology 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