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 1921–1930 of 3304 papers

TitleStatusHype
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN featuresCode0
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Weakly-correlated synapses promote dimension reduction in deep neural networks—0
Estimating Model Uncertainty of Neural Networks in Sparse Information Form—0
Training (Overparametrized) Neural Networks in Near-Linear Time—0
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding DistributionCode0
Latent variable modeling with random featuresCode1
Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition—0
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems—0
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure—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