SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 291–300 of 3304 papers

TitleStatusHype
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe—0
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images—0
3D Face Recognition with Sparse Spherical Representations—0
A Characterization of the Non-Uniqueness of Nonnegative Matrix Factorizations—0
Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Method for Classifying Snow Using Ski-Mounted Strain Sensors—0
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
Additive Component Analysis—0
A mechanism-driven reinforcement learning framework for shape optimization of airfoils—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