SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2341–2350 of 3304 papers

TitleStatusHype
Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation—0
Maximum Margin Principal Components—0
May the force be with you—0
Measures of Entropy from Data Using Infinitely Divisible Kernels—0
Measuring and modeling the motor system with machine learning—0
Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms—0
Measuring inter-cluster similarities with Alpha Shape TRIangulation in loCal Subspaces (ASTRICS) facilitates visualization and clustering of high-dimensional data—0
Towards Measuring Place Function Similarity at Fine Spatial Granularity with Trajectory Embedding—0
Measuring the Data—0
Mechanisms of dimensionality reduction and decorrelation in deep neural networks—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