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 1981–1990 of 3304 papers

TitleStatusHype
Sampling and multilevel coarsening algorithms for fast matrix approximations—0
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text—0
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks—0
Scalable Adaptive Stochastic Optimization Using Random Projections—0
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data—0
Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems—0
Scalable Differentially Private Clustering via Hierarchically Separated Trees—0
Scalable Iterative Algorithm for Robust Subspace Clustering—0
Scalable manifold learning by uniform landmark sampling and constrained locally linear embedding—0
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks—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