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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1001–1025 of 3304 papers

TitleStatusHype
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural NetworksCode0
On Sufficient Graphical Models—0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Differential Privacy for Clustering Under Continual Observation—0
Principal subbundles for dimension reduction—0
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value RegularizationCode0
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)—0
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—0
Learning Environment Models with Continuous Stochastic Dynamics—0
Feature Selection: A perspective on inter-attribute cooperation—0
Long-term Conversation Analysis: Exploring Utility and PrivacyCode0
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data—0
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNECode0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
On the use of the Gram matrix for multivariate functional principal components analysisCode0
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and SparsificationCode0
Relating tSNE and UMAP to Classical Dimensionality Reduction—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—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