SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2841–2850 of 3304 papers

TitleStatusHype
Audio Visual Speech Recognition using Deep Recurrent Neural Networks—0
Information-Theoretic Bounds and Approximations in Neural Population Coding—0
Quantum Laplacian Eigenmap—0
Disentangling Topic Models: A Cross-cultural Analysis of Personal Values through Words—0
A Category Space Approach to Supervised Dimensionality Reduction—0
Bayesian latent structure discovery from multi-neuron recordingsCode0
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation—0
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data—0
Representation Learning with Deconvolution for Multivariate Time Series Classification and VisualizationCode0
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity—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