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 1451–1500 of 3304 papers

TitleStatusHype
A Probabilistic Graph Coupling View of Dimension Reduction—0
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models—0
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings—0
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set MethodCode0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Knowledge Base Index Compression via Dimensionality and Precision Reduction—0
Encoding large information structures in linear algebra and statistical modelsCode0
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer—0
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
An Introduction to Autoencoders—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—0
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra—0
Machine-Learning the Classification of Spacetimes—0
Modelling matrix time series via a tensor CP-decomposition—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Exact Post-selection Inference For Tracking S&P500—0
Uniform-in-Phase-Space Data Selection with Iterative Normalizing FlowsCode0
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
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