SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1791–1800 of 3304 papers

TitleStatusHype
Pointed subspace approach to incomplete data—0
Policy Optimization for PDE Control with a Warm Start—0
Policy Search with High-Dimensional Context Variables—0
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach—0
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation—0
Practical Algorithms for Learning Near-Isometric Linear Embeddings—0
Precise expressions for random projections: Low-rank approximation and randomized Newton—0
Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI—0
Precision-Recall Balanced Topic Modelling—0
Predicting Inflation with Recurrent Neural Networks—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