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
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
An Embedding is Worth a Thousand Noisy LabelsCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature DescriptorsCode1
Learning Wasserstein EmbeddingsCode1
Less is more: Faster and better music version identification with embedding distillationCode1
Light Curve Classification with DistClassiPy: a new distance-based classifierCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
catch22: CAnonical Time-series CHaracteristicsCode1
ManifoldNet: A Deep Network Framework for Manifold-valued DataCode1
CatBoost: gradient boosting with categorical features supportCode1
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
A local approach to parameter space reduction for regression and classification tasksCode1
Deep reconstruction of strange attractors from time seriesCode1
DefakeHop: A Light-Weight High-Performance Deepfake DetectorCode1
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
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
Going Beyond T-SNE: Exposing whatlies in Text EmbeddingsCode1
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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