SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1971–1980 of 3304 papers

TitleStatusHype
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—0
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems—0
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation—0
HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions—0
Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model—0
Jointly Modeling and Clustering Tensors in High Dimensions—0
Heteroscedastic Max-Min Distance Analysis—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—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