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 1971–1980 of 3304 papers

TitleStatusHype
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Estimating Model Uncertainty of Neural Networks in Sparse Information Form—0
Training (Overparametrized) Neural Networks in Near-Linear Time—0
Weakly-correlated synapses promote dimension reduction in deep neural networks—0
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding DistributionCode0
Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition—0
Precise expressions for random projections: Low-rank approximation and randomized Newton—0
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure—0
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems—0
Isometric Autoencoders—0
Show:102550
← PrevPage 198 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