SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3051–3075 of 3304 papers

TitleStatusHype
Learning Fair Representations for Kernel ModelsCode0
Learning Feature Sparse Principal SubspaceCode0
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature RepresentationCode0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
Number Representations in LLMs: A Computational Parallel to Human PerceptionCode0
Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewCode0
Vector Diffusion Maps and the Connection LaplacianCode0
Learning from the past, predicting the statistics for the future, learning an evolving systemCode0
Deep Continuous ClusteringCode0
Numerical simulation, clustering and prediction of multi-component polymer precipitationCode0
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel LearningCode0
Learning Integral Representations of Gaussian ProcessesCode0
Random-projection ensemble dimension reductionCode0
Simple and Effective Dimensionality Reduction for Word EmbeddingsCode0
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Objective discovery of dominant dynamical processes with intelligible machine learningCode0
Random Projection in Deep Neural NetworksCode0
Featurizing Koopman Mode Decomposition For Robust ForecastingCode0
Whitening-Free Least-Squares Non-Gaussian Component AnalysisCode0
Convergent autoencoder approximation of low bending and low distortion manifold embeddingsCode0
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal EmbeddingsCode0
Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methodsCode0
TriMap: Large-scale Dimensionality Reduction Using TripletsCode0
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology PatchesCode0
Learning Low-Level Causal Relations using a Simulated Robotic ArmCode0
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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