SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1901–1910 of 3304 papers

TitleStatusHype
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
Reduced-Rank Multi-objective Policy Learning and Optimization—0
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Reducing training requirements through evolutionary based dimension reduction and subject transfer—0
Regression-aware decompositions—0
Regression of high dimensional angular momentum states of light—0
Regularisation for PCA- and SVD-type matrix factorisations—0
Regularized Pooling—0
Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes—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