SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2541–2550 of 3304 papers

TitleStatusHype
Quantum classification of the MNIST dataset with Slow Feature Analysis—0
Spectral feature scaling method for supervised dimensionality reduction—0
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly—0
Distribution-based Label Space Transformation for Multi-label Learning—0
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
Transcription Factor-DNA Binding Via Machine Learning Ensembles—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining—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