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 1521–1530 of 3304 papers

TitleStatusHype
Efficient Contextual Representation Learning Without Softmax Layer—0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Are Latent Factor Regression and Sparse Regression Adequate?—0
Efficient channel charting via phase-insensitive distance computation—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
Brain Inspired Face Recognition: A Computational Framework—0
Highly comparative feature-based time-series classification—0
High-Order Low-Rank Tensors for Semantic Role Labeling—0
How to Use less Features and Reach Better Performance in Author Gender Identification—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