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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1031–1040 of 3304 papers

TitleStatusHype
Bayesian Non-linear Latent Variable Modeling via Random Fourier FeaturesCode0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High DimensionsCode0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
G-invariant diffusion maps—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—0
Differentially private sliced inverse regression in the federated paradigm—0
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data AnalysisCode0
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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