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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 2901–2950 of 3304 papers

TitleStatusHype
Deep Learning with Nonparametric ClusteringCode0
Wassmap: Wasserstein Isometric Mapping for Image Manifold LearningCode0
Universal Feature Selection Tool (UniFeat): An Open-Source Tool for Dimensionality ReductionCode0
Enhancing Affinity Propagation for Improved Public Sentiment InsightsCode0
Enhancing Dimension-Reduced Scatter Plots with Class and Feature CentroidsCode0
Bayesian calibration of stochastic agent based model via random forestCode0
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating TheoremCode0
Self-Taught Convolutional Neural Networks for Short Text ClusteringCode0
Product Manifold LearningCode0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
Beyond the Nucleus: Cytoplasmic Dominance in Follicular Thyroid Carcinoma Detection Using Single-Cell Raman Imaging Across Multiple DevicesCode0
Semantic Relatedness Based Re-ranker for Text SpottingCode0
Enhancing Sufficient Dimension Reduction via Hellinger CorrelationCode0
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image RetrievalCode0
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative FactorsCode0
Visualizing DNA reaction trajectories with deep graph embedding approachesCode0
Cluster Exploration using Informative Manifold ProjectionsCode0
Backprojection for Training Feedforward Neural Networks in the Input and Feature SpacesCode0
Optimal Projections for Discriminative Dictionary Learning using the JL-lemmaCode0
Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarityCode0
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoderCode0
Entropic Wasserstein Component AnalysisCode0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulationCode0
Deep learning to discover and predict dynamics on an inertial manifoldCode0
Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set MethodCode0
A Deep Learning Framework for Assessing Physical Rehabilitation ExercisesCode0
Classifying herbal medicine origins by temporal and spectral data mining of electronic noseCode0
Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction NetworksCode0
A Practical Algorithm for Topic Modeling with Provable GuaranteesCode0
Network Representation Learning: Consolidation and Renewed BearingCode0
Semi-supervised Embedding Learning for High-dimensional Bayesian OptimizationCode0
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear FormsCode0
Deep Learning of Conjugate MappingsCode0
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activationsCode0
Joint Embedding of GraphsCode0
Neural Codes for Image RetrievalCode0
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood AnalysisCode0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Kernel Feature Selection via Conditional Covariance MinimizationCode0
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
Deep Kernel Principal Component Analysis for Multi-level Feature LearningCode0
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality ReductionCode0
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksCode0
Layered Models can "Automatically" Regularize and Discover Low-Dimensional Structures via Feature LearningCode0
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial SystemsCode0
Semi-Supervised Graph Learning Meets Dimensionality ReductionCode0
Pseudocell Tracer—A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombinationCode0
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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