SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1360 of 3304 papers

TitleStatusHype
Finding Rule-Interpretable Non-Negative Data Representation—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Augmentation Component Analysis: Modeling Similarity via the Augmentation OverlapsCode0
AVIDA: Alternating method for Visualizing and Integrating Data—0
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs—0
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNECode0
PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes—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