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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 2001–2025 of 3304 papers

TitleStatusHype
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Self-calibrating Neural Networks for Dimensionality Reduction—0
Self-Expressive Decompositions for Matrix Approximation and Clustering—0
Self-paced Principal Component Analysis—0
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers—0
Self-Supervised Graph Embedding Clustering—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning—0
SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning—0
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining—0
Semi-supervised deep learning for high-dimensional uncertainty quantification—0
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection—0
Semi-supervised Deep Representation Learning for Multi-View Problems—0
Semi-supervised Fisher vector network—0
Semi-supervised Learning based on Distributionally Robust Optimization—0
Semi-supervised Learning with Explicit Relationship Regularization—0
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings—0
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction—0
Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits—0
Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization—0
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity—0
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives—0
Sequential Dimensionality Reduction for Extracting Localized Features—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