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 391–400 of 3304 papers

TitleStatusHype
A Semiparametric Approach to Interpretable Machine Learning—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction—0
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding—0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Approximate Matrix Multiplication with Application to Linear Embeddings—0
Split Semantic Detection in Sandplay Images—0
Accelerating hyperbolic t-SNE—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre—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