SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1191–1200 of 3304 papers

TitleStatusHype
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Exact Post-selection Inference For Tracking S&P500—0
Exchangeability, Conformal Prediction, and Rank Tests—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
Exemplar-Centered Supervised Shallow Parametric Data Embedding—0
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization—0
Expected Information Gain Estimation via Density Approximations: Sample Allocation and Dimension Reduction—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond—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