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 1891–1900 of 3304 papers

TitleStatusHype
Evaluation of company investment value based on machine learning—0
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Deep Monocular Visual Odometry for Ground Vehicle—0
Compact Learning for Multi-Label Classification—0
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection—0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
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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