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 1071–1080 of 3304 papers

TitleStatusHype
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
A Comparison of Neural Network Training Methods for Text Classification—0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Embedding Compression for Efficient Re-Identification—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
A New Approach for Scalable Analysis of Microbial Communities—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
Capacity Analysis of Vector Symbolic Architectures—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—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