SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 176–200 of 3304 papers

TitleStatusHype
Kernelized Diffusion mapsCode1
Bayesian Optimization of Sampling Densities in MRICode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
A local approach to parameter space reduction for regression and classification tasksCode1
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Learning the dynamics of technical trading strategiesCode1
Learning Wasserstein EmbeddingsCode1
Less is more: Faster and better music version identification with embedding distillationCode1
CatBoost: gradient boosting with categorical features supportCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate modelsCode1
ManifoldNet: A Deep Network Framework for Manifold-valued DataCode1
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
Deep reconstruction of strange attractors from time seriesCode1
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Neural Decomposition: Functional ANOVA with Variational AutoencodersCode1
OmiEmbed: a unified multi-task deep learning framework for multi-omics dataCode1
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer CompressionCode1
ParaDime: A Framework for Parametric Dimensionality ReductionCode1
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
Generative locally linear embedding: A module for manifold unfolding and visualizationCode1
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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