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 1951–2000 of 3304 papers

TitleStatusHype
Graphon Pooling in Graph Neural Networks—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images—0
Graph Signal Representation with Wasserstein Barycenters—0
Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change—0
GRASPEL: Graph Spectral Learning at Scale—0
Grassmann Averages for Scalable Robust PCA—0
Grassmann Graph Embedding—0
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data—0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering—0
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction—0
GroupEnc: encoder with group loss for global structure preservation—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Group Preserving Label Embedding for Multi-Label Classification—0
Γ-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data—0
Hand Gesture Recognition with Leap Motion—0
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—0
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems—0
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation—0
HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions—0
Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model—0
Jointly Modeling and Clustering Tensors in High Dimensions—0
Heteroscedastic Max-Min Distance Analysis—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering—0
Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries—0
Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets—0
Hierarchical Trait-State Model for Decoding Dyadic Social Interactions—0
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB—0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction—0
Highly comparative feature-based time-series classification—0
High-Order Low-Rank Tensors for Semantic Role Labeling—0
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts—0
Hilbert space embeddings and metrics on probability measures—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