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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 2351–2375 of 3304 papers

TitleStatusHype
catch22: CAnonical Time-series CHaracteristicsCode1
Representation Transfer for Differentially Private Drug Sensitivity Prediction—0
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español—0
Stochastic Linear Bandits with Hidden Low Rank Structure—0
On the cross-validation bias due to unsupervised pre-processingCode0
Comparing of Term Clustering Frameworks for Modular Ontology Learning—0
Empowering individual trait prediction using interactions—0
Unsupervised speech representation learning using WaveNet autoencodersCode1
Coupling the reduced-order model and the generative model for an importance sampling estimator—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problemsCode0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Image retrieval method based on CNN and dimension reduction—0
A witness function based construction of discriminative models using Hermite polynomials—0
Transfer Representation Learning with TSK Fuzzy System—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
Performance prediction of data streams on high-performance architecture—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
Active Learning with TensorBoard Projector—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—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