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 1741–1750 of 3304 papers

TitleStatusHype
Event detection in Colombian security Twitter news using fine-grained latent topic analysis—0
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems—0
Evolutionary Echo State Network: evolving reservoirs in the Fourier space—0
Exact Post-selection Inference For Tracking S&P500—0
Exchangeability, Conformal Prediction, and Rank Tests—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
Exemplar-Centered Supervised Shallow Parametric Data Embedding—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Expected Information Gain Estimation via Density Approximations: Sample Allocation and Dimension Reduction—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Show:102550
← PrevPage 175 of 331Next →
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