SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2571–2580 of 3304 papers

TitleStatusHype
A Functional approach for Two Way Dimension Reduction in Time Series—0
A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
A Generalized Mean Approach for Distributed-PCA—0
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data—0
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
A Geometric take on Metric Learning—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
Aggregated Deep Local Features for Remote Sensing Image Retrieval—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