SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2501–2510 of 3304 papers

TitleStatusHype
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets—0
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions—0
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activationsCode0
Model-Free Context-Aware Word Composition—0
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern DataCode0
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—0
Premise selection with neural networks and distributed representation of featuresCode0
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Prototype Discovery using Quality-Diversity—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