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 2711–2720 of 3304 papers

TitleStatusHype
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines—0
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images—0
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection—0
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—0
Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code—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