SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 151–160 of 3304 papers

TitleStatusHype
Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture—0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
Beyond Worst-Case Dimensionality Reduction for Sparse Vectors—0
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality ReductionCode0
BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor—0
Empirical likelihood approach for high-dimensional moment restrictions with dependent dataCode0
From Small to Large Language Models: Revisiting the Federalist PapersCode0
Achieving Fair PCA Using Joint Eigenvalue Decomposition—0
Rewards-based image analysis in microscopy—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—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