SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2231–2240 of 3304 papers

TitleStatusHype
Learning Semantics and Selectional Preference of Adjective-Noun Pairs—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Learning Stochastic Dynamical Systems with Structured Noise—0
Learning Stochastic Representations of Physical Systems—0
Learning Treatment Representations for Downstream Instrumental Variable Regression—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction—0
Learning Word Meta-Embeddings—0
Lee and Seung (2000)'s Algorithms for Non-negative Matrix Factorization: A Supplementary Proof Guide—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