SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1371–1380 of 3304 papers

TitleStatusHype
Consistent Representation Learning for High Dimensional Data Analysis—0
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery—0
Fuzzy Pooling—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning—0
Cone-Constrained Principal Component Analysis—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—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