SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2361–2370 of 3304 papers

TitleStatusHype
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problemsCode0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Image retrieval method based on CNN and dimension reduction—0
A witness function based construction of discriminative models using Hermite polynomials—0
Transfer Representation Learning with TSK Fuzzy System—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
Performance prediction of data streams on high-performance architecture—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—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