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 651–700 of 3304 papers

TitleStatusHype
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Sparsifying dimensionality reduction of PDE solution data with Bregman learning—0
Oblivious subspace embeddings for compressed Tucker decompositions—0
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning—0
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance AnalysisCode0
Macroscopic Market Making Games via Multidimensional Decoupling Field—0
Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking—0
VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region AnnotationCode0
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension—0
Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from SimulationsCode0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
Noisy Data Visualization using Functional Data Analysis—0
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty QuantificationCode0
Random Subspace Local Projections—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
A comparison of correspondence analysis with PMI-based word embedding methodsCode0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
Enhancing Sufficient Dimension Reduction via Hellinger CorrelationCode0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
On the Connection Between Non-negative Matrix Factorization and Latent Dirichlet Allocation—0
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach—0
NUTS, NARS, and Speech—0
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE—0
Canonical Variates in Wasserstein Metric Space—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Embedding Compression for Efficient Re-Identification—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
Rank Reduction Autoencoders—0
Automated Anomaly Detection on European XFEL Klystrons—0
Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging—0
An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Lens functions for exploring UMAP Projections with Domain KnowledgeCode0
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits—0
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured SystemsCode0
Distributional Reference Class Forecasting of Corporate Sales Growth With Multiple Reference Variables—0
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data—0
Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey—0
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning—0
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanismsCode0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
QUACK: Quantum Aligned Centroid KernelCode0
Utilizing Machine Learning and 3D Neuroimaging to Predict Hearing Loss: A Comparative Analysis of Dimensionality Reduction and Regression Techniques—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