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 1531–1540 of 3304 papers

TitleStatusHype
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts—0
Hilbert space embeddings and metrics on probability measures—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Efficient channel charting via phase-insensitive distance computation—0
Hodge Laplacians and Hodge Diffusion Maps—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Horizontal and Vertical Attention in Transformers—0
Brain Inspired Face Recognition: A Computational Framework—0
Show:102550
← PrevPage 154 of 331Next →
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