SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2951–2960 of 3304 papers

TitleStatusHype
Local High-order Regularization on Data Manifolds—0
Semi-supervised Learning with Explicit Relationship Regularization—0
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting—0
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images—0
Reducing training requirements through evolutionary based dimension reduction and subject transfer—0
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets—0
Dimensionality Reduction via Regression in Hyperspectral Imagery—0
Principal Polynomial Analysis—0
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial PoolerCode0
Model-Coupled Autoencoder for Time Series Visualisation—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