SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 676–700 of 3304 papers

TitleStatusHype
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