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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 1901–1950 of 3304 papers

TitleStatusHype
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data ProcessingCode0
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and SurveyCode0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying densityCode0
PCA Reduced Gaussian Mixture Models with Applications in SuperresolutionCode0
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Sufficient Dimension Reduction for Average Causal Effect Estimation—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—0
ECG Beats Fast Classification Base on Sparse DictionariesCode0
A Perturbation-Based Kernel Approximation FrameworkCode0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
Screening Rules and its Complexity for Active Set Identification—0
An approximate solution for options market-making in high dimension—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images—0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model—0
Exact and Approximation Algorithms for Sparse PCACode0
Clustering small datasets in high-dimension by random projection—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Bayesian neural networks and dimensionality reduction—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Understanding Brain Dynamics for Color Perception using Wearable EEG headband—0
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Survey: Geometric Foundations of Data Reduction—0
Physical Action Categorization using Signal Analysis and Machine Learning—0
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion—0
Feature Clustering for Support Identification in Extreme Regions—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Review of Swarm Intelligence-based Feature Selection Methods—0
Modal Principal Component Analysis—0
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model—0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction—0
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems—0
Dimensionality Reduction for k-means Clustering—0
Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems—0
Image-Based Benchmarking and Visualization for Large-Scale Global Optimization—0
Dimension reduction in recurrent networks by canonicalization—0
Spectral estimation from simulations via sketching—0
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data—0
In search of the weirdest galaxies in the UniverseCode0
Numerical simulation, clustering and prediction of multi-component polymer precipitationCode0
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating TheoremCode0
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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