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 1051–1060 of 3304 papers

TitleStatusHype
Elementos da teoria de aprendizagem de máquina supervisionada—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference—0
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction—0
A new method color MS-BSIF Features learning for the robust kinship verification—0
Causal learning with sufficient statistics: an information bottleneck approach—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
Efficient Learning and Planning with Compressed Predictive States—0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—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