SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1691–1700 of 3304 papers

TitleStatusHype
On Sufficient Graphical Models—0
On the Computation and Applications of Large Dense Partial Correlation Networks—0
On the Connection Between Non-negative Matrix Factorization and Latent Dirichlet Allocation—0
On the consistency theory of high dimensional variable screening—0
On the Correspondence between Gaussian Processes and Geometric Harmonics—0
On the dimension of pullback attractors in recurrent neural networks—0
On-the-fly spectral unmixing based on Kalman filtering—0
On the Importance of Distraction-Robust Representations for Robot Learning—0
On the Needs for Rotations in Hypercubic Quantization Hashing—0
On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets—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