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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 1026–1050 of 3304 papers

TitleStatusHype
Compression supports low-dimensional representations of behavior across neural circuits—0
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations—0
Identifying Chemicals Through Dimensionality Reduction—0
Transform Once: Efficient Operator Learning in Frequency DomainCode1
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications—0
Kernel PCA for multivariate extremes—0
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
EVNet: An Explainable Deep Network for Dimension ReductionCode1
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant AnalysisCode0
A Generalized EigenGame with Extensions to Multiview Representation LearningCode0
Graceful Forgetting II. Data as a Process—0
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction—0
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection—0
Data Dimension Reduction makes ML Algorithms efficient—0
RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language ModelsCode2
Topology of cognitive maps—0
Inverse Kernel DecompositionCode0
Minimalist Data Wrangling with PythonCode1
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Genomics Data Analysis via Spectral Shape and Topology—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