SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 851–900 of 3304 papers

TitleStatusHype
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—0
Differentially private sliced inverse regression in the federated paradigm—0
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data AnalysisCode0
Multiscale Flow for Robust and Optimal Cosmological Analysis—0
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving—0
Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization—0
ShaRP: Shape-Regularized Multidimensional ProjectionsCode0
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization—0
Dictionary Learning under Symmetries via Group Representations—0
Label Embedding via Low-Coherence Matrices—0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Dimensionality Reduction for General KDE Mode Finding—0
Cooperative Thresholded Lasso for Sparse Linear Bandit—0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
On the Noise Sensitivity of the Randomized SVDCode0
Classic machine learning methods—0
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
Efficient Multi-Scale Attention Module with Cross-Spatial LearningCode2
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
Contrastive inverse regression for dimension reduction—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
Functional sufficient dimension reduction through information maximization with application to classification—0
State Representation Learning Using an Unbalanced AtlasCode0
Spectral Clustering via Orthogonalization-Free MethodsCode0
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method—0
Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reductionCode0
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
Agile gesture recognition for capacitive sensing devices: adapting on-the-job—0
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional DatasetsCode1
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
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
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes—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
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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