SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 3304 papers

TitleStatusHype
Adversarial AutoencodersCode1
Approximating Likelihood Ratios with Calibrated Discriminative ClassifiersCode1
Tensor Canonical Correlation Analysis for Multi-view Dimension ReductionCode1
Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
Active Learning for Manifold Gaussian Process RegressionCode0
Distributed Lyapunov Functions for Nonlinear NetworksCode0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—0
Local Averaging Accurately Distills Manifold Structure From Noisy Data—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques—0
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-ResolutionCode0
Let the Tree Decide: FABART A Non-Parametric Factor Model—0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions—0
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—0
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
Enabling stratified sampling in high dimensions via nonlinear dimensionality reductionCode0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—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