SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1611–1620 of 3304 papers

TitleStatusHype
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Rethinking Spatial Dimensions of Vision TransformersCode1
Model Order Reduction based on Runge-Kutta Neural Network—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Measuring and modeling the motor system with machine learning—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