SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1761–1770 of 3304 papers

TitleStatusHype
Blind signal decomposition of various word embeddings based on join and individual variance explained—0
t-EVA: Time-Efficient t-SNE Video Annotation—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Dimensionality reduction, regularization, and generalization in overparameterized regressionsCode0
Manifold Partition Discriminant Analysis—0
Locally Linear Embedding and its Variants: Tutorial and SurveyCode1
Angular Embedding: A New Angular Robust Principal Component Analysis—0
Accelerating Text Mining Using Domain-Specific Stop Word Lists—0
Optimizing Offer Sets in Sub-Linear Time—0
Strong and weak principles of neural dimension reduction—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