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 1741–1750 of 3304 papers

TitleStatusHype
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data VisualizationCode1
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorizationCode0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
Multitask machine learning of collective variables for enhanced sampling of rare events—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare—0
A journey in ESN and LSTM visualisations on a language taskCode0
K-Deep Simplex: Deep Manifold Learning via Local DictionariesCode0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor EmbeddingCode0
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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