SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 101–125 of 3304 papers

TitleStatusHype
Path Development Network with Finite-dimensional Lie Group RepresentationCode1
Transformers for 1D Signals in Parkinson's Disease Detection from GaitCode1
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality ReductionCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed DataCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
SLISEMAP: Supervised dimensionality reduction through local explanationsCode1
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOMCode1
Triangle Attack: A Query-efficient Decision-based Adversarial AttackCode1
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximationCode1
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
The chemical space of terpenes: insights from data science and AICode1
TLDR: Twin Learning for Dimensionality ReductionCode1
Nonnegative spatial factorizationCode1
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)Code1
Clustering with UMAP: Why and How Connectivity MattersCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
A local approach to parameter space reduction for regression and classification tasksCode1
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate modelsCode1
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid SimulationsCode1
Generative locally linear embedding: A module for manifold unfolding and visualizationCode1
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate CurvatureCode1
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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