SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 801–825 of 3304 papers

TitleStatusHype
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction EmbeddingsCode1
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality ReductionCode0
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels—0
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text—0
A Statistical View of Column Subset SelectionCode0
Heuristic Hyperparameter Choice for Image Anomaly Detection—0
On Optimality in ROVir—0
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Two Approaches to Supervised Image Segmentation—0
A Computational Topology-based Spatiotemporal Analysis Technique for Honeybee AggregationCode0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Company2Vec -- German Company Embeddings based on Corporate Websites—0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural NetworksCode0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
On Sufficient Graphical Models—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Differential Privacy for Clustering Under Continual Observation—0
Principal subbundles for dimension reduction—0
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value RegularizationCode0
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)—0
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—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