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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 1876–1900 of 3304 papers

TitleStatusHype
Modal Principal Component Analysis—0
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model—0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction—0
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems—0
Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems—0
Dimensionality Reduction for k-means Clustering—0
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization—0
Dimension reduction in recurrent networks by canonicalization—0
Spectral estimation from simulations via sketching—0
Improving the HardNet DescriptorCode1
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data—0
In search of the weirdest galaxies in the UniverseCode0
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance ComputingCode2
Numerical simulation, clustering and prediction of multi-component polymer precipitationCode0
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating TheoremCode0
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political PartiesCode0
Attention or memory? Neurointerpretable agents in space and time—0
Linear Tensor Projection Revealing Nonlinearity—0
Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes dataCode1
Manifold Learning via Manifold Deflation—0
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology PatchesCode0
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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