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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 2051–2100 of 3304 papers

TitleStatusHype
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Multivariate time-series modeling with generative neural networks—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
Fair Principal Component Analysis and Filter Design—0
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Optimal estimation of sparse topic models—0
ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making PathsCode0
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection—0
ShapeVis: High-dimensional Data Visualization at Scale—0
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality ReductionCode0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy—0
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization—0
MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation—0
Upper bounds for Model-Free Row-Sparse Principal Component Analysis—0
Estimating Model Uncertainty of Neural Network in Sparse Information Form—0
Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms—0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational AutoencodersCode0
Learned SVD: solving inverse problems via hybrid autoencoding—0
Deep learning to discover and predict dynamics on an inertial manifoldCode0
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection—0
Bounded Manifold Completion—0
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender SystemsCode0
Projection Pursuit with Applications to scRNA Sequencing Data—0
MM Algorithms for Distance Covariance based Sufficient Dimension Reduction and Sufficient Variable Selection—0
From deep learning to mechanistic understanding in neuroscience: the structure of retinal predictionCode0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Discriminative Dimension Reduction based on Mutual Information—0
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Self Organizing Nebulous Growths for Robust and Incremental Data VisualizationCode0
Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiomeCode0
Hybrid Kronecker Product Decomposition and Approximation—0
On Distance and Kernel Measures of Conditional Independence—0
Using Dimensionality Reduction to Optimize t-SNECode0
Dimensionality reduction: theoretical perspective on practical measures—0
Solving Interpretable Kernel Dimensionality Reduction—0
Precision-Recall Balanced Topic Modelling—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
Logical Interpretations of Autoencoders—0
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling—0
GRASPEL: Graph Spectral Learning at Scale—0
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—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