SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 101150 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
Deep Learning for Functional Data Analysis with Adaptive Basis LayersCode1
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Improving Metric Dimensionality Reduction with Distributed TopologyCode1
HUMAP: Hierarchical Uniform Manifold Approximation and ProjectionCode1
Unsupervised Behaviour Discovery with Quality-Diversity OptimisationCode1
Large-scale optimal transport map estimation using projection pursuitCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate CurvatureCode1
WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise LabelsCode1
Estimating leverage scores via rank revealing methods and randomizationCode1
A hyperparameter-tuning approach to automated inverse planningCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering TransformsCode1
ProsoBeast Prosody Annotation ToolCode1
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word RepresentationsCode1
Generative Locally Linear EmbeddingCode1
Rethinking Spatial Dimensions of Vision TransformersCode1
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny SubspacesCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Revisiting Dynamic Convolution via Matrix DecompositionCode1
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration MethodCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified