SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 601–625 of 3304 papers

TitleStatusHype
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein—0
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data—0
GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component AnalysisCode0
Run-to-Run Control With Bayesian Optimization for Soft Landing of Short-Stroke Reluctance Actuators—0
A Big Data Architecture for Early Identification and Categorization of Dark Web SitesCode0
Accelerating hyperbolic t-SNE—0
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces—0
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries—0
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks—0
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Comprehensive OOD Detection Improvements—0
Content-Aware Tweet Location Inference using Quadtree Spatial Partitioning and Jaccard-Cosine Word Embedding—0
Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings—0
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm—0
Sparse PCA with False Discovery Rate Controlled Variable Selection—0
Image Similarity using An Ensemble of Context-Sensitive ModelsCode0
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning—0
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spacesCode1
MISS: Multiclass Interpretable Scoring SystemsCode0
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Fun with Flags: Robust Principal Directions via Flag ManifoldsCode0
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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