SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2401–2425 of 3304 papers

TitleStatusHype
GAN-EM: GAN based EM learning framework—0
Robust Subspace Approximation in a Stream—0
Model-based targeted dimensionality reduction for neuronal population data—0
Manifold Coordinates with Physical MeaningCode0
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology—0
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Enhanced Expressive Power and Fast Training of Neural Networks by Random ProjectionsCode0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Subspace Clustering through Sub-ClustersCode0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Unsupervised learning with contrastive latent variable modelsCode0
Interactive dimensionality reduction using similarity projections—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Matrix Product Operator Restricted Boltzmann Machines—0
Semi-supervised Deep Representation Learning for Multi-View Problems—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction—0
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering—0
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projectionsCode0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—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