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 501–525 of 3304 papers

TitleStatusHype
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design—0
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease—0
Analyzing movies to predict their commercial viability for producers—0
A Hybrid Approach for Binary Classification of Imbalanced Data—0
BDEC:Brain Deep Embedded Clustering model—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
An Analysis of the t-SNE Algorithm for Data Visualization—0
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Applying Ricci Flow to High Dimensional Manifold Learning—0
Applying Graph-based Keyword Extraction to Document Retrieval—0
A hierarchical approach with feature selection for emotion recognition from speech—0
A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—0
Accelerated Canonical Polyadic Decomposition by Using Mode Reduction—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
CASS: Cross Adversarial Source Separation via Autoencoder—0
Chasing Collective Variables using Autoencoders and biased trajectories—0
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Applications of machine learning to predict seasonal precipitation for East Africa—0
Application Research On Real-Time Perception Of Device Performance Status—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