SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2501–2510 of 3304 papers

TitleStatusHype
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—0
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems—0
A cognitive based Intrusion detection system—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
A Compact and Discriminative Face Track Descriptor—0
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques—0
A Comparative Study of Text Preprocessing Approaches for Topic Detection of User Utterances—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Comparison of Neural Network Training Methods for Text Classification—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