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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 3151–3175 of 3304 papers

TitleStatusHype
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Dimensionality Reduction of Massive Sparse Datasets Using Coresets—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Dimensionality reduction techniques to support insider trading detection—0
Dimensionality reduction: theoretical perspective on practical measures—0
Dimensionality Reduction using Elastic Measures—0
Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis—0
Dimensionality Reduction Using the Sparse Linear Model—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
Dimensionality reduction via path integration for computing mRNA distributions—0
Dimensionality Reduction via Regression in Hyperspectral Imagery—0
Dimensionality reduction with missing values imputation—0
Dimensionality reduction with subgaussian matrices: a unified theory—0
Dimensionality Reduction with Subspace Structure Preservation—0
Dimension Estimation Using Autoencoders—0
Dimension Estimation Using Random Connection Models—0
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems—0
Dimension Reduction for Fréchet Regression—0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
Stochastic neighborhood embedding and the gradient flow of relative entropy—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis—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