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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 601–625 of 3304 papers

TitleStatusHype
Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays—0
On latent dynamics learning in nonlinear reduced order modeling—0
PAGE: Parametric Generative Explainer for Graph Neural NetworkCode0
ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms—0
Robust spectral clustering with rank statistics—0
OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data—0
Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment AnalysisCode0
"Normalized Stress" is Not Normalized: How to Interpret Stress CorrectlyCode0
Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines—0
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling—0
Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics—0
Towards aerodynamic surrogate modeling based on β-variational autoencodersCode0
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds—0
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions—0
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN—0
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image SegmentationCode0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Principal component analysis balancing prediction and approximation accuracy for spatial dataCode0
Feature Clock: High-Dimensional Effects in Two-Dimensional Plots—0
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
On-the-fly spectral unmixing based on Kalman filtering—0
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems—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