SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1351–1400 of 3304 papers

TitleStatusHype
Machine learning discovery of new phases in programmable quantum simulator snapshotsCode0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Funnels: Exact maximum likelihood with dimensionality reductionCode0
Robust factored principal component analysis for matrix-valued outlier accommodation and detection—0
Risk and optimal policies in bandit experiments—0
Triangle Attack: A Query-efficient Decision-based Adversarial AttackCode1
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
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximationCode1
Dimensionality Reduction for Categorical Data—0
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data—0
Low-complexity Rounded KLT Approximation for Image Compression—0
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
Dimension Reduction with Prior Information for Knowledge DiscoveryCode0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
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
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record DataCode0
Gaussian Determinantal Processes: a new model for directionality in data—0
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial SystemsCode0
ELBD: Efficient score algorithm for feature selection on latent variables of VAE—0
A Data Quarantine Model to Secure Data in Edge Computing—0
Three-body renormalization group limit cycles based on unsupervised feature learning—0
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape DescriptorCode0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Active Linear Regression for _p Norms and Beyond—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
ExClus: Explainable Clustering on Low-dimensional Data Representations—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting—0
Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
The chemical space of terpenes: insights from data science and AICode1
GenURL: A General Framework for Unsupervised Representation Learning—0
Adaptive Weighted Multi-View Clustering—0
Merging Two Cultures: Deep and Statistical Learning—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—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