SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2431–2440 of 3304 papers

TitleStatusHype
Contrastive Multivariate Singular Spectrum Analysis—0
Unsupervised Dimension Selection using a Blue Noise Spectrum—0
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
Scaling Gaussian Process Regression with DerivativesCode0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets—0
Perceptual Visual Interactive Learning—0
CatBoost: gradient boosting with categorical features supportCode1
Modified Multidimensional Scaling and High Dimensional Clustering—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