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 581–590 of 3304 papers

TitleStatusHype
Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing DataCode0
Training Artificial Neural Networks by Coordinate Search Algorithm—0
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space—0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised LearningCode0
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps—0
An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf Optimizer (GWO) for text feature selection and clustering—0
Evaluating the Stability of Deep Learning Latent Feature Spaces—0
Fast Data-driven Greedy Sensor Selection for Ridge Regression—0
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering—0
Guided Quantum Compression for High Dimensional Data ClassificationCode0
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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