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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 801–850 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 with Non-degrading Generalization—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
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network—0
Dimensionality Reduction and Prioritized Exploration for Policy Search—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
Classification with Repulsion Tensors: A Case Study on Face Recognition—0
Deep topic modeling by multilayer bootstrap network and lasso—0
Deep Triphone Embedding Improves Phoneme Recognition—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
Deep Variational Sufficient Dimensionality Reduction—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data—0
A survey of dimensionality reduction techniques based on random projection—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Demixed Principal Component Analysis—0
A Survey on Archetypal Analysis—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
Demystifying Embedding Spaces using Large Language Models—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—0
Density-based Isometric Mapping—0
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization—0
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis—0
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
A framework for streamlined statistical prediction using topic models—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