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
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data—0
A Bilinear Programming Approach for Multiagent Planning—0
A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms—0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
A Light weight and Hybrid Deep Learning Model based Online Signature Verification—0
Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification—0
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Deep matrix factorizations—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
Deep neural networks for the evaluation and design of photonic devices—0
A study of semantic augmentation of word embeddings for extractive summarization—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
DeepRT: deep learning for peptide retention time prediction in proteomics—0
Deep Sufficient Representation Learning via Mutual Information—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Deep topic modeling by multilayer bootstrap network and lasso—0
Deep Triphone Embedding Improves Phoneme Recognition—0
Differentially private sliced inverse regression in the federated paradigm—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