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Dimensionality Reduction

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

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Papers

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Showing 1401–1450 of 3304 papers

TitleStatusHype
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping—0
Construction of neural networks for realization of localized deep learning—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Generalized Principal Component Analysis—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
Supervised tensor decomposition with features on multiple modes—0
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup—0
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices—0
Consistent Representation Learning for High Dimensional Data Analysis—0
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—0
Generative Modeling: A Review—0
Generative Modeling for Atmospheric Convection—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
Cone-Constrained Principal Component Analysis—0
Genomics Data Analysis via Spectral Shape and Topology—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Free Lunch for Gait Recognition: A Novel Relation Descriptor—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
Geometric Machine Learning on EEG Signals—0
Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects—0
Geometry-Aware Hamiltonian Variational Auto-Encoder—0
Geometry of Deep Learning for Magnetic Resonance Fingerprinting—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension—0
G-invariant diffusion maps—0
GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies—0
Global explainability of a deep abstaining classifier—0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Golden Ratio-Based Sufficient Dimension Reduction—0
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Graceful Forgetting II. Data as a Process—0
Gradient-based kernel method for feature extraction and variable selection—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
Fourier analysis perspective for sufficient dimension reduction problem—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—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