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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 1201–1225 of 3304 papers

TitleStatusHype
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
Deep-gKnock: nonlinear group-feature selection with deep neural network—0
A Semiparametric Approach to Interpretable Machine Learning—0
Algorithm-Agnostic Interpretations for Clustering—0
Adaptive Metric Dimensionality Reduction—0
Accelerating hyperbolic t-SNE—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
A selective review of sufficient dimension reduction for multivariate response regression—0
Deep Gaussian Mixture Models—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference—0
Deep Features for training Support Vector Machine—0
A Robust Approach for Securing Audio Classification Against Adversarial Attacks—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
Adaptive Locally Linear Embedding—0
Deep Embedding Kernel—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
A review of unsupervised learning in astronomy—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
2D+3D facial expression recognition via embedded tensor manifold regularization—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