SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2691–2700 of 3304 papers

TitleStatusHype
An Introduction to Autoencoders—0
An Investigation of Newton-Sketch and Subsampled Newton Methods—0
An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework—0
An iterative coordinate descent algorithm to compute sparse low-rank approximations—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics—0
A non-parametric conditional factor regression model for high-dimensional input and response—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—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