SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2251–2260 of 3304 papers

TitleStatusHype
Time Series Featurization via Topological Data Analysis—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
tLaSDI: Thermodynamics-informed latent space dynamics identification—0
TL-PCA: Transfer Learning of Principal Component Analysis—0
TMI: Thermodynamic inference of data manifolds—0
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets—0
Topic Identification and Discovery on Text and Speech—0
Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations: Clustering and Sorting with Varying Cell-Cell Adhesion—0
Topological Indoor Mapping through WiFi Signals—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