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 1851–1875 of 3304 papers

TitleStatusHype
ECG Beats Fast Classification Base on Sparse DictionariesCode0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
A Perturbation-Based Kernel Approximation FrameworkCode0
Screening Rules and its Complexity for Active Set Identification—0
Going Beyond T-SNE: Exposing whatlies in Text EmbeddingsCode1
An approximate solution for options market-making in high dimension—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images—0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model—0
Exact and Approximation Algorithms for Sparse PCACode0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Clustering small datasets in high-dimension by random projection—0
Bayesian neural networks and dimensionality reduction—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Understanding Brain Dynamics for Color Perception using Wearable EEG headband—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Survey: Geometric Foundations of Data Reduction—0
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Physical Action Categorization using Signal Analysis and Machine Learning—0
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion—0
Feature Clustering for Support Identification in Extreme Regions—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Review of Swarm Intelligence-based Feature Selection Methods—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