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 1926–1950 of 3304 papers

TitleStatusHype
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
On Path Integration of Grid Cells: Group Representation and Isotropic ScalingCode1
Precise expressions for random projections: Low-rank approximation and randomized Newton—0
Isometric Autoencoders—0
Dimensionality reduction via path integration for computing mRNA distributions—0
Supervised Visualization for Data Exploration—0
Markov-Lipschitz Deep LearningCode1
Sample complexity and effective dimension for regression on manifolds—0
Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective OptimizationCode0
Deep Dimension Reduction for Supervised Representation LearningCode1
An Augmented Translation Technique for low Resource language pair: Sanskrit to Hindi translation—0
Human brain activity for machine attention—0
Novelty Detection via Robust Variational AutoencodingCode0
Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction—0
A Semiparametric Approach to Interpretable Machine Learning—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
Generalized Penalty for Circular Coordinate RepresentationCode0
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Semi-supervised deep learning for high-dimensional uncertainty quantification—0
Unsupervised Feature Selection via Multi-step Markov Transition Probability—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