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Dimensionality Reduction

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

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Papers

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Showing 3101–3150 of 3304 papers

TitleStatusHype
Diffeomorphic brain shape modelling using Gauss-Newton optimisation—0
Diffeomorphic Dimensionality Reduction—0
Differentially private sliced inverse regression in the federated paradigm—0
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm—0
Differential Privacy for Clustering Under Continual Observation—0
Differential Privacy Meets Neural Network Pruning—0
Difficulty in estimating visual information from randomly sampled images—0
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
Diffusion Maps meet Nyström—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
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
Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm—0
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network—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
Dimensionality reduction for acoustic vehicle classification with spectral embedding—0
Dimensionality Reduction for Categorical Data—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Dimensionality Reduction for General KDE Mode Finding—0
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation—0
Dimensionality Reduction for k-means Clustering—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
Dimensionality reduction for time series data—0
Dimensionality Reduction for Tukey Regression—0
Dimensionality Reduction for Wasserstein Barycenter—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer—0
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
Dimensionality reduction methods for molecular simulations—0
Dimensionality Reduction of Affine Variational Inequalities Using Random Projections—0
Dimensionality Reduction of Collective Motion by Principal Manifolds—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