SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1901–1925 of 3304 papers

TitleStatusHype
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data ProcessingCode0
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and SurveyCode0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying densityCode0
PCA Reduced Gaussian Mixture Models with Applications in SuperresolutionCode0
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Sufficient Dimension Reduction for Average Causal Effect Estimation—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—0
ECG Beats Fast Classification Base on Sparse DictionariesCode0
A Perturbation-Based Kernel Approximation FrameworkCode0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
Screening Rules and its Complexity for Active Set Identification—0
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
Clustering small datasets in high-dimension by random projection—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Bayesian neural networks and dimensionality reduction—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Understanding Brain Dynamics for Color Perception using Wearable EEG headband—0
Show:102550
← PrevPage 77 of 133Next →
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