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 2991–3000 of 3304 papers

TitleStatusHype
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs—0
Cost-informed dimensionality reduction for structural digital twin technologies—0
Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion—0
Coupling the reduced-order model and the generative model for an importance sampling estimator—0
Covariance Matrix Adaptation Evolution Strategy Assisted by Principal Component Analysis—0
Covariance matrix preparation for quantum principal component analysis—0
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications—0
Cover Learning for Large-Scale Topology Representation—0
Creation of a Deep Convolutional Auto-Encoder in Caffe—0
Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach—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