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 1381–1390 of 3304 papers

TitleStatusHype
A Data Quarantine Model to Secure Data in Edge Computing—0
Three-body renormalization group limit cycles based on unsupervised feature learning—0
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape DescriptorCode0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Active Linear Regression for _p Norms and Beyond—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting—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