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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 876–900 of 3304 papers

TitleStatusHype
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks—0
Diffusion Fingerprints—0
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Diffusion Maps : Using the Semigroup Property for Parameter Tuning—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Digital Fingerprinting of Microstructures—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
DimenFix: A novel meta-dimensionality reduction method for feature preservation—0
Dimensionality-reduced subspace clustering—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
A Unified Framework for Optimization-Based Graph Coarsening—0
Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm—0
Classic machine learning methods—0
Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate—0
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
Dimensionality Reduction and Prioritized Exploration for Policy Search—0
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs—0
Dimensionality Reduction and Wasserstein Stability for Kernel Regression—0
Dimensionality Reduction as Probabilistic Inference—0
Dimensionality reduction based on Distance Preservation to Local Mean (DPLM) for SPD matrices and its application in BCI—0
Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification—0
Dimensionality Reduction Ensembles—0
Likelihood Contribution based Multi-scale Architecture for Generative Flows—0
An Impossibility Theorem for Node Embedding—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