SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1476–1500 of 3304 papers

TitleStatusHype
Manifold learning via quantum dynamics—0
Machine learning discovery of new phases in programmable quantum simulator snapshotsCode0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Funnels: Exact maximum likelihood with dimensionality reductionCode0
Risk and optimal policies in bandit experiments—0
Robust factored principal component analysis for matrix-valued outlier accommodation and detection—0
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design—0
Joint Characterization of the Cryospheric Spectral Feature Space—0
Dimensionality Reduction for Categorical Data—0
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
Low-complexity Rounded KLT Approximation for Image Compression—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Dimension Reduction with Prior Information for Knowledge DiscoveryCode0
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian InferenceCode0
An Attack on Facial Soft-biometric Privacy Enhancement—0
Machine Learning Based Forward Solver: An Automatic Framework in gprMaxCode0
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness—0
Feature selection or extraction decision process for clustering using PCA and FRSD—0
Gaussian Determinantal Processes: a new model for directionality in data—0
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record DataCode0
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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