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 2701–2710 of 3304 papers

TitleStatusHype
Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction—0
Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images—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
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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