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 3101–3110 of 3304 papers

TitleStatusHype
基於稀疏表示之語者識別 (Sparse Representation Based Speaker Identification) [In Chinese]—0
Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction—0
Unsupervised Bump Hunting Using Principal Components—0
A Deep Graph Embedding Network Model for Face Recognition—0
A theoretical contribution to the fast implementation of null linear discriminant analysis method using random matrix multiplication with scatter matrices—0
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models—0
Statistical and computational trade-offs in estimation of sparse principal components—0
Diffusion Fingerprints—0
Dimensionality Reduction of Affine Variational Inequalities Using Random Projections—0
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—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