SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 871–880 of 3304 papers

TitleStatusHype
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm—0
Differential Privacy for Clustering Under Continual Observation—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Difficulty in estimating visual information from randomly sampled images—0
A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition—0
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks—0
Diffusion Fingerprints—0
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—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