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 1051–1100 of 3304 papers

TitleStatusHype
Cooperative Thresholded Lasso for Sparse Linear Bandit—0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
On the Noise Sensitivity of the Randomized SVDCode0
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
Classic machine learning methods—0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
Contrastive inverse regression for dimension reduction—0
Functional sufficient dimension reduction through information maximization with application to classification—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
State Representation Learning Using an Unbalanced AtlasCode0
Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reductionCode0
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method—0
Spectral Clustering via Orthogonalization-Free MethodsCode0
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders—0
Agile gesture recognition for capacitive sensing devices: adapting on-the-job—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction—0
Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing—0
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction—0
K-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning—0
A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach—0
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes—0
Wasserstein Dictionaries of Persistence Diagrams—0
The Power of Typed Affine Decision Structures: A Case Study—0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability—0
A Method for Classifying Snow Using Ski-Mounted Strain Sensors—0
Functional Diffusion MapsCode0
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical PhenomenaCode0
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals—0
Ellipsoid fitting with the Cayley transformCode0
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints—0
Dynamic Security Region of Natural Gas Systems in Integrated Electricity-Gas Systems—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Wireless Channel Charting: Theory, Practice, and Applications—0
A Neural Network Transformer Model for Composite Microstructure Homogenization—0
Dimensionality Reduction as Probabilistic Inference—0
Online Recognition of Incomplete Gesture Data to Interface Collaborative RobotsCode0
Gradient-Free Textual Inversion—0
Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning—0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
OFTER: An Online Pipeline for Time Series Forecasting—0
Multi-Linear Kernel Regression and Imputation in Data Manifolds—0
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets—0
NeuroDAVIS: A neural network model for data visualizationCode0
Maximum Covariance Unfolding Regression: A Novel Covariate-based Manifold Learning Approach for Point Cloud Data—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
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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