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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 3176–3200 of 3304 papers

TitleStatusHype
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Dimension reduction for model-based clustering—0
Dimension Reduction for time series with Variational AutoEncoders—0
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection—0
Dimension reduction in recurrent networks by canonicalization—0
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
Dimension Reduction Using Active Manifolds—0
Dimension Reduction via Colour Refinement—0
Dimension reduction via score ratio matching—0
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures—0
Dimension Reduction with Non-degrading Generalization—0
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis—0
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction—0
Direction and Constraint in Phenotypic Evolution: Dimension Reduction and Global Proportionality in Phenotype Fluctuation and Responses—0
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders—0
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification—0
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods—0
Discovery of sustainable energy materials via the machine-learned material space—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—0
Discriminative convolutional Fisher vector network for action recognition—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