SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2901–2925 of 3304 papers

TitleStatusHype
Classic machine learning methods—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Classification of Cervical Cancer Dataset—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Classification with Repulsion Tensors: A Case Study on Face Recognition—0
Class Mean Vector Component and Discriminant Analysis—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
Clustering small datasets in high-dimension by random projection—0
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics—0
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics—0
Cognitive Coding of Speech—0
Collaborative causal inference on distributed data—0
Collaborative Gaussian Processes for Preference Learning—0
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—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