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 1991–2000 of 3304 papers

TitleStatusHype
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB—0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction—0
Highly comparative feature-based time-series classification—0
High-Order Low-Rank Tensors for Semantic Role Labeling—0
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts—0
Hilbert space embeddings and metrics on probability measures—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