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 2711–2720 of 3304 papers

TitleStatusHype
Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms—0
Supervised Dimensionality Reduction for Big DataCode0
Word Embeddings as Features for Supervised Coreference Resolution—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future PerspectivesCode0
Dynamic Tensor Clustering—0
Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data—0
Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer—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