SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1501–1510 of 3304 papers

TitleStatusHype
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering—0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised LearningCode0
fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
Interactive Dimensionality Reduction for Comparative AnalysisCode0
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey—0
Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution—0
Self-paced Principal Component Analysis—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